Postural behavior is an integral response of animals t o complex environmental factors. Huddling, nearly contacting one another on the side, and spreading are common postural behaviors of group-housed animals undergoing cold, comfortable, and w a r h o t sensations, respectively. These postural patterns have been routinely used by animal caretakers to assess thermal comfort of the animals and to make according adjustment on the environmental settings or management schemes. This manual adjustment approach, however, has the inherent limitations of daily discontinuity and inconsistency between caretakers in interpretation of the animal comfort behavior. The goal of this project was to explore a novel, automated image analysis system that would assess the thermal comfort of swine and make proper environmental adjustments to enhance animal well- being and production efficiency. This paper describes the progress and on-going work toward the achievement of our proposed goal. The feasibility of classifying the thermal comfort state of young pigs by neural network (NN) analysis of their postural images was first examined. It included exploration of using certain feature selections of the postural behavioral images as the input to a three-layer NN that was trained to classify the corresponding thermal comfort state as being cold, comfortable, or warm. The image feature selections, a critical step for the classification, examined in this study included Fourier coefficient (FC), moment ( M ) , perimeter and area (P&A), and combination of M and P&A of the processed binary postural images. The result was positive, with the combination of M and P&A as the input feature to the NN yielding the highest correct classification rate. Subsequent work included the development of hardware and computational algorithms that enable automatic image segmentation, motion detection, and the selection of the behavioral images suitable for use in the classification. Work is in progress to quantify the relationships of postural behavior and physiological responses of pigs using thermographs. The results are expected to facilitate objective training of NN, hence improving the accuracy of the postural image-based assessment of the thermal comfort state. Work is also in progress to implement the analysis and assessment algorithms into computer codes for real-time application.