Automatic target classification in general is complicated owing to the influence of pose, articulation, and overall viewing geometry on two dimensional SAR data. Three dimensional (3D) data, however, affords the opportunity to develop robust classification techniques independent of those issues. Based on geometric invariants, discriminants can be obtained assuming the target or its phase center lattice can be well modelled by 3D geometries subject to independent rigid body motions, (i.e. reflection, rotation, and translation). Toward this end, we present recent results in the development of a unique 3D classification algorithm. The concepts herein are developed for the full 3D observation space. In particular, we discuss several discrimination metrics based on a target's geometry. As such, they are necessarily invariant to pose and articulation and
consequently provide robust classification performance. These geometric-invariant discriminants are concisely expressed as equations unique to a single target structure, or to the spatial interrelationships of multiple structures (this addresses the articulation problem). Once established, these equations can subsequently be used to properly classify the structure or structures at a later time without the need for explicit knowledge of the 3D orientation of the structures within the field of view. We present the mathematical basis behind these classification schemes, discuss implementation concepts, and finish by demonstrating these techniques on synthetic data.