Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study were composed of votes for each U.S. Representative on 16 key items during a particular legislative session. Experiment 2 employed a dataset developed in our human neuroscience laboratory and focused on the effects of sympathetic nervous system arousal on cardiac and inner-ear physiology. Experiment 3 employed the well-known Wisconsin Breast Cancer dataset, which was used to develop a sensitive, automated diagnostic method of distinguishing between malignant and benign cells. We suggest that the SOM is capable of identifying cohesive patterns of nonlinear measurements that would be difficult to identify using traditional linear data reduction procedures and that neural networks will be increasingly valuable in the analysis of a wide range of complex behaviors. (Contains 6 figures.)