UDAT is a pattern recognition tool for mass analysis of various types of data, including image and audio. Based on its WND-CHARM (ascl:1312.002) prototype, UDAT computed a large set of numerical content descriptors from each file it analyzes, and selects the most informative features using statistical analysis. The tool can perform automatic classification of galaxy images by training with annotated galaxy images. It also has unsupervised learning capabilities, such as query-by-example of galaxies based on morphology. That is, given an input galaxy image of interest, the tool can search through a large database of images to retrieve the galaxies that are the most similar to the query image. The downside of the tool is its computational complexity, which in most cases will require a small or medium cluster.