Nuclear movements during meiotic prophase, driven by cytoskeleton forces, are a broadly conserved mechanism in opisthokonts and plants to promote pairing between homologous chromosomes. These forces are transmitted to the chromosomes by specific associations between telomeres and the nuclear envelope during meiotic prophase. Defective chromosome movements (CMs) harm pairing and recombination dynamics between homologues, thereby affecting faithful gametogenesis. For this reason, modelling the behaviour of CMs and their possible microvariations as a result of mutations or physico-chemical stress is important to understand this crucial stage of meiosis. Current developments in high-throughput imaging and image processing are yielding large CM datasets that are suitable for data mining approaches. To facilitate adoption of data mining pipelines, we present  ChroMo , an interactive, unsupervised cloud application specifically designed for exploring CM datasets from live imaging.  ChroMo  contains a wide selection of algorithms and visualizations for time-series segmentation, motif discovery, and assessment of causality networks. Using  ChroMo  to analyse meiotic CMs in fission yeast, we found previously undiscovered features of CMs and causality relationships between chromosome morphology and trajectory.  ChroMo  will be a useful tool for understanding the behaviour of meiotic CMs in yeast and other model organisms.