Large computational complexity arises in model-based ATR systems because an object's image is typically a function of several degrees of freedom, such as target class, pose, articulation, configuration and sensor geometry. Most model- based ATR systems treat this dependency by incorporating an exhaustive search through a library of image views. This approach, however, requires enormous storage and extensive search processing. Some ATR systems reduce the size of the library by forming composite averaged images at the expense of reducing the captured pose specific information, usually resulting in a decrease in performance. The Linear Signal Decomposition/Direction of Arrival (LSD/DOA) system, on the other hand, forms a reduced-size, essential-information object data set which implicitly incorporates target and sensor variation specific data. This reduces ATR processing by providing a low computational-cost indexing function with little loss of discrimination and pose estimation performance. The LSD/DOA system consists of two independent components: a computationally expensive off-line component which forms the object representation and a computationally inexpensive on-line object recognition component. The size of the stored data set may also be adjusted, providing a means to trade off complexity versus performance. The focus of this paper will be the performance of the LSD/DOA ATR against the MSTAR (public) data set.