An ongoing study of automatic target recognition using a passive multisensor suite is presented. The multisensor suite consists of electro-optical and acoustic sensors, a radio frequency interferometer, and meteorological sensors. Three feature analysis tools-a genetic algorithm, a branch-and-bound algorithm, and a new class overlap region partitioning scheme (CORPS)-are employed for assessing the effectiveness of features extracted by several data and feature fusion concepts. Dedicated hardware has been configured for laboratory and field testing. A high-resolution graphics display has been designed for monitoring the target-acquisition-related processes and maximizing information transfer to the operator.