This paper describes two applications of a model based target recognition approach that employs an efficient interpretation tree search algorithm for matching 3D model features to sensed features extracted from 2D imagery. The algorithm is tolerant to missing and incomplete features and makes optimal use of geometric constraints to greatly reduce search time while guaranteeing an optimal match. When a target is recognized, a minimum error estimate of its location is made. The algorithm requires that a rough estimate of the sensor view point be available prior to matching. Two applications of this algorithm are described, one for FLIR and one for SAR processing. For the FLIR application 3D models are constructed using linear space curves, quadratic space curves, and quadric surfaces. The features extracted from the imagery are straight line segments, representing the projection of linear space curves, and conic sections, representing the projection of quadratic space curves, and quadric surface limbs. For the SAR application the target models specify the 3D location of model scatterers, and the sensed features are the 2D locations of the returns detected in the SAR image. In the SAR application, the target was assumed to be mobile so special processing was necessary to handle the initial view point uncertainty. Experimental test results of these applications are described.