diff --git a/pycytominer/cyto_utils/features.py b/pycytominer/cyto_utils/features.py
index a55d012..11cb946 100644
--- a/pycytominer/cyto_utils/features.py
+++ b/pycytominer/cyto_utils/features.py
@@ -90,3 +90,41 @@ def infer_cp_features(population_df, metadata=False):
     ), "No CP features found. Are you sure this dataframe is from CellProfiler?"
 
     return features
+
+
+def drop_outlier_features(
+    population_df, features="infer", samples="none", outlier_cutoff=15
+):
+    """
+    Exclude a feature if its min or max absolute value is greater than the threshold
+
+    Arguments:
+    population_df - pandas DataFrame that includes metadata and observation features
+    features - a list of features present in the population dataframe [default: "infer"]
+               if "infer", then assume cell painting features are those that start with
+               "Cells_", "Nuclei_", or "Cytoplasm_"
+    samples - list samples to perform operation on
+              [default: "none"] - if "none", use all samples to calculate
+    outlier_cutoff - threshold to remove feature if absolute value is greater
+
+    Return:
+    list of features to exclude from the population_df
+    """
+    # Subset dataframe
+    if samples != "none":
+        population_df = population_df.loc[samples, :]
+
+    if features == "infer":
+        features = infer_cp_features(population_df)
+        population_df = population_df.loc[:, features]
+    else:
+        population_df = population_df.loc[:, features]
+
+    max_feature_values = population_df.max().abs()
+    min_feature_values = population_df.min().abs()
+
+    outlier_features = max_feature_values[
+        (max_feature_values > outlier_cutoff) | (min_feature_values > outlier_cutoff)
+    ].index.tolist()
+
+    return outlier_features
diff --git a/pycytominer/feature_select.py b/pycytominer/feature_select.py
index fe64e3a..a31faf9 100644
--- a/pycytominer/feature_select.py
+++ b/pycytominer/feature_select.py
@@ -9,7 +9,11 @@ from pycytominer.correlation_threshold import correlation_threshold
 from pycytominer.variance_threshold import variance_threshold
 from pycytominer.get_na_columns import get_na_columns
 from pycytominer.cyto_utils.output import output
-from pycytominer.cyto_utils.features import get_blacklist_features, infer_cp_features
+from pycytominer.cyto_utils.features import (
+    get_blacklist_features,
+    infer_cp_features,
+    drop_outlier_features,
+)
 
 
 def feature_select(
@@ -26,6 +30,7 @@ def feature_select(
     compression=None,
     float_format=None,
     blacklist_file=None,
+    outlier_cutoff=15,
 ):
     """
     Performs feature selection based on the given operation
@@ -52,12 +57,17 @@ def feature_select(
     blacklist_file - file location of dataframe with features to exclude [default: None]
                      Note that if "blacklist" in operation then will remove standard
                      blacklist
+    outlier_cutoff - the threshold at which the maximum or minimum value of a feature
+                     across a full experiment is excluded [default: 15]. Note that this
+                     procedure is typically applied (and therefore the default is
+                     suitable) for after normalization.
     """
     all_ops = [
         "variance_threshold",
         "correlation_threshold",
         "drop_na_columns",
         "blacklist",
+        "drop_outliers",
     ]
 
     # Make sure the user provides a supported operation
@@ -114,6 +124,13 @@ def feature_select(
                 )
             else:
                 exclude = get_blacklist_features(population_df=profiles)
+        elif op == "drop_outliers":
+            exclude = drop_outlier_features(
+                population_df=profiles,
+                features=features,
+                samples=samples,
+                outlier_cutoff=outlier_cutoff,
+            )
 
         excluded_features += exclude
 

diff --git a/pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py b/pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py
new file mode 100644
index 0000000..ab190de
--- /dev/null
+++ b/pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py
@@ -0,0 +1,55 @@
+import os
+import random
+import pytest
+import tempfile
+import warnings
+import pandas as pd
+from pycytominer.cyto_utils.features import drop_outlier_features
+
+# Build data to use in tests
+data_df = pd.DataFrame(
+    {
+        "Metadata_plate": ["a", "a", "a", "a", "b", "b", "b", "b"],
+        "Metadata_treatment": [
+            "drug",
+            "drug",
+            "control",
+            "control",
+            "drug",
+            "drug",
+            "control",
+            "control",
+        ],
+        "Cells_x": [1, 2, -8, 2, 5, 5, 5, -1],
+        "Cytoplasm_y": [3, -1, 7, 4, 5, -9, 6, 1],
+        "Nuclei_z": [-1, 8, 2, 5, -6, 20, 2, -2],
+        "Cells_zz": [14, -46, 1, 60, -30, -100, 2, 2],
+    }
+).reset_index(drop=True)
+
+
+def test_outlier_default():
+    result = drop_outlier_features(data_df)
+    expected_result = ["Cells_zz", "Nuclei_z"]
+    assert sorted(result) == sorted(expected_result)
+
+
+def test_outlier_high_cutoff():
+    result = drop_outlier_features(data_df, outlier_cutoff=30)
+    expected_result = ["Cells_zz"]
+    assert result == expected_result
+
+
+def test_outlier_samples():
+    result = drop_outlier_features(data_df, samples=[0, 1, 2, 3, 5])
+    expected_result = ["Cells_zz", "Nuclei_z"]
+    assert sorted(result) == sorted(expected_result)
+
+    result = drop_outlier_features(data_df, samples=[0, 1, 2, 3])
+    expected_result = ["Cells_zz"]
+    assert result == expected_result
+
+
+def test_outlier_features():
+    result = drop_outlier_features(data_df, features=["Cells_x", "Cytoplasm_y"])
+    assert len(result) == 0
diff --git a/pycytominer/tests/test_feature_select.py b/pycytominer/tests/test_feature_select.py
index 51bfa95..11692e4 100644
--- a/pycytominer/tests/test_feature_select.py
+++ b/pycytominer/tests/test_feature_select.py
@@ -52,6 +52,27 @@ data_unique_test_df = pd.DataFrame(
 ).reset_index(drop=True)
 
 
+data_outlier_df = pd.DataFrame(
+    {
+        "Metadata_plate": ["a", "a", "a", "a", "b", "b", "b", "b"],
+        "Metadata_treatment": [
+            "drug",
+            "drug",
+            "control",
+            "control",
+            "drug",
+            "drug",
+            "control",
+            "control",
+        ],
+        "Cells_x": [1, 2, -8, 2, 5, 5, 5, -1],
+        "Cytoplasm_y": [3, -1, 7, 4, 5, -9, 6, 1],
+        "Nuclei_z": [-1, 8, 2, 5, -6, 20, 2, -2],
+        "Cells_zz": [14, -46, 1, 60, -30, -100, 2, 2],
+    }
+).reset_index(drop=True)
+
+
 def test_feature_select_get_na_columns():
     """
     Testing feature_select and get_na_columns pycytominer function
@@ -273,3 +294,25 @@ def test_feature_select_blacklist():
     )
     expected_result = pd.DataFrame({"y": [1, 2, 8, 5, 2, 1], "zz": [0, -3, 8, 9, 6, 9]})
     pd.testing.assert_frame_equal(result, expected_result)
+
+
+def test_feature_select_drop_outlier():
+    """
+    Testing feature_select and get_na_columns pycytominer function
+    """
+    result = feature_select(
+        data_outlier_df, features="infer", operation="drop_outliers"
+    )
+    expected_result = data_outlier_df.drop(["Cells_zz", "Nuclei_z"], axis="columns")
+    pd.testing.assert_frame_equal(result, expected_result)
+
+    result = feature_select(
+        data_outlier_df, features="infer", operation="drop_outliers", outlier_cutoff=30
+    )
+    expected_result = data_outlier_df.drop(["Cells_zz"], axis="columns")
+    pd.testing.assert_frame_equal(result, expected_result)
+
+    result = feature_select(
+        data_outlier_df, features=["Cells_x", "Cytoplasm_y"], operation="drop_outliers"
+    )
+    pd.testing.assert_frame_equal(result, data_outlier_df)

Extreme Outlier Features Remain after Feature Selection
In broadinstitute/pooled-cell-painting-analysis#57 @bethac07 noticed that some of the heatmaps didn't look quite right and suspected that only a handful of features were dominating the signal. We observed this effect in other projects as well.

We may want to consider adding an additional feature selection step that optionally drops these exploding features.
pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py::test_outlier_default pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py::test_outlier_high_cutoff pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py::test_outlier_samples pycytominer/tests/test_cyto_utils/test_feature_drop_outlier.py::test_outlier_features pycytominer/tests/test_feature_select.py::test_feature_select_get_na_columns pycytominer/tests/test_feature_select.py::test_feature_select_get_na_columns_feature_infer pycytominer/tests/test_feature_select.py::test_feature_select_variance_threshold pycytominer/tests/test_feature_select.py::test_feature_select_correlation_threshold pycytominer/tests/test_feature_select.py::test_feature_select_all pycytominer/tests/test_feature_select.py::test_feature_select_compress pycytominer/tests/test_feature_select.py::test_feature_select_blacklist pycytominer/tests/test_feature_select.py::test_feature_select_drop_outlier