The National Institute of Mental Health has identified the need for methods of assessing emotional behavior to assist the tracking of normal and deviant developmental processes. In response to this need, American Research Corporation of Virginia (ARCOVA) proposes the development of a neural network-based machine vision system for evaluation of emotional state from facial expression. Research comparing the relative contribution of expressive channels in humans has shown that observers use information from facial cues more than any other source to determine an individual's emotional frame of mind. Specific aims of the research include extraction of edge features from black and white video imagery of human faces, selection of optimal neural network paradigms for extraction and analysis of facial features (such as eyes, eye brows and mouth), training and testing of the selected neural networks, and design of an interactive user interface. The significance of this effort is the innovative use of neural networks for facial feature extraction and analysis. Since neural networks have been shown to be more effective at pattern recognition tasks than traditional methods in similar applications, ARCOVA will demonstrate a proof-of-concept system for automated measurement of human emotion which is superior to present techniques.