Model-based Automatic Target Recognition (ATR) algorithms are adept at recognizing targets in high fidelity 3D LADAR imagery. Most current approaches involve a matching component where a hypothesized target and target pose are iteratively aligned to pre-segmented range data. Once the model-to-data alignment has converged, a match score is generated indicating the quality of match. This score is then used to rank one model hypothesis over another. The main drawback of this approach is twofold. First, to ensure the correct target is recognized, a large number of model hypotheses must be considered. Even with a highly accurate indexing algorithm, the number of target types and variants that need to be explored is prohibitive for real-time operation. Second, the iterative matching step must consider a variety of target poses to ensure that the correct alignment is recovered. Inaccurate alignments produce erroneous match scores and thus errors when ranking one target hypothesis over another. To compensate for such drawbacks, we explore the use of situational awareness information already available to an image analyst. Examples of such information include knowledge of the surrounding terrain (to assess potential occlusion levels) and targets of interest (to account for target variants).