The method we have been using is based on our Self-Organizing Hierarchical Optimal Subspace Learning and Inference Framework (SHOSLIF). It uses the theories of linear discriminant projection for automatic optimal feature selection in each of the internal nodes of a Space-Tessellation Tree. In this paper, we present our recent study on the applicability of the approach to variability in position, size, and 3D orientation. In the work presented here, we require "well-framed" images os input for recognition. By well-framed images we mean that only a relatively small variation in the size, position, and orientation of the objects in the input images is allowed. We report the experimental results that show the performance difference between the subspaces of linear discriminant analysis and the principle component analysis and the effect of using a tree as opposed to a flat eigenspace.