A multiple sensor approach to automatically detecting targets viewed by a Forward-Looking Infrared (FLIR) sensor and a range sensor is described. The system developed used sensor-dependent processing to segment, possible targets in the images, measure features for segmented regions, and analyze the single sensor feature information. The post-segmentation target detection problem was that of separating segmented targets from segmented non-targets. Segmented regions in both images were geometrically registered, and a novel multiple sensor feature, called the correspondence feature, was measured. The correspondence feature exploited the observation that targets occupy the same space in both types of image, while segmented non-target regions do not tend to behave in this manner. The detection problem was modeled as a two-class decision problem where the classes were target and non-target. The Bayesian minimum error criterion was used as the class estimation rule. Two single sensor and three multiple sensor approaches to target detection were developed, and their performance compared. Performance improvements are described which resulted from incorporating the correspondence feature information into the class estimation process. Results were tabulated for performance on a data base of real, corresponding FLIR and range images composed of 97 FLIR and 57 range images.