Bayesian Binning (BB) is an exact inference technique which was originally developed for applications in Computational Neuroscience, e.g. modeling spike count distributions or estimating peri-stimulus time histograms (PSTH). BB encodes a (conditional) probability distribution (or density) which is piecewise constant in the domain of interest. This suggests that BB might be useful for retrospective temporal segmentation tasks, too. We illustrate the potential usefulness of BB for temporal segmentation on two examples. First, we segment neural spike train data, demonstrating that BB is able to locate change points in the PSTH correctly. Second, we employ BB for (human) action sequence segmentation. We show that BB accurately identifies the transition points in the action sequence (e.g. a change from ’walking’ to ’jumping’).