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We use DIRSIG to evaluate algorithms for recognizing 3D objects defined by faces of different orientations and different materials. The experiments consider varying object pose as well as variable environmental conditions. Objects are represented using subspaces defined for the 0.4-2.5 micron spectral range. Spatial resolutions are considered that provide mixtures of multiple object surfaces and background. For recognizing 3-D objects in cluttered backgrounds, the orthogonal projection ratio (OPR) is proposed to minimize the effects of noise and approximation error. The experiments consider varying object pose as well as variable environmental conditions. Background clutter is represented using spectral subspaces that are estimated from the image data. The experiments consider the recognition of several 3D objects with various geometries and surface materials. Both desert and urban scenes are considered as well as a range of ground spatial distances.