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A three-dimensional (3D) target detection and recognition algorithm, using the biologically-inspired Map-Seeking Circuit (MSC), is implemented to efficiently solve the typical template matching problem in computer vision. Given a 3D template model of a vehicle, this prototype locates the vehicle in a two-dimensional (2D) panchromatic image and determines its pose (i.e. viewing azimuth, elevation, scale, and in-plane rotation). In our implementation, we introduce a detection stage followed by the spawning of multiple MSC processes in parallel to classify and determine the pose of the detection candidates. Our implementation increases the speed of detection and allows efficient classification when multiple targets are present in the same image. We present promising results after applying our algorithm to challenging real world test imagery.