Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-­ derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-­ derived cell culture composition, we have implemented an imaging assay based on cell painting and deep learning to recognize cell types in dense and mixed cultures with high fidelity. The approach holds promise for use in the quality control of iPSC-­derived cell culture models and can be used for documenting semi-stable states in heterogeneous and plastic cell cultures.