This paper continues the study reported in Ref. 1 and Ref. 2 trading off the fundamental ATR performance capability (i.e., algorithm-independent) of various SAR design options. The previous papers considered the performance impact of SAR range/cross-range resolution and compared the use of 1-D HRR (high-range-resolution radar) versus 2-D SAR, versus multisensor, 3-D SAR. The work reported here extends the SAR and HRR results of Ref. 2 to include aspect diversity in the SAR measurements. We show that SAR and HRR are benefited by multi-aspect measurements mostly because multiple views add diversity: poorer views benefit from having better views combined in a multi-aspect classifier. Finally, as a proof of concept, multi-aspect diversity is incorporated into an existing SAR ATR classifier; performance of an MSTAR 10-class MSE classifier is shown to improve substantially. A major tenet is verified by the experimental results: added measurement domains, such as aspect diversity, which separate the target signature vectors in the observation space, make it easier to obtain better target classification, enhanced false- alarm rejection, and robustness to unknown statistics.