AbstractSummaryImage-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualization tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses.AvailabilityCellProfiler Analyst binaries for Windows and MacOS are freely available for download athttps://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available athttps://github.com/CellProfiler/CellProfiler-Analyst/. A sample dataset is available athttps://cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection.