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Neural networks have shown strong potential in research and in healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where more data are typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately 100 images per condition to classify 6 different genetic conditions. We analyzed both preprocessed images that were cropped to show only the skin lesions as well as more complex images showing features such as the entire body segment, the person, and/or the background. The classifier construction process included attribution methods to visualize which pixels were most important for computer-based classification. Our classifier was significantly more accurate than pediatricians or medical geneticists for both types of images and suggests steps for further research involving clinical scenarios and other applications., Competing Interests: B.D.S.: Editor-in-Chief of the American Journal of Medical Genetics; previously (until 2019) employee of GeneDx, Inc., a genetic/genomic testing laboratory that is a subsidiary of Opko Health, and previous holder of Opko Health stock options; previously (until 2019) member of Scientific Advisory Board for FDNA. All other authors declare no competing interests.