We develop and evaluate robust model-based approaches to SAR ground vehicle combat ID by fusing multiple looks of the same vehicle collected at different angles. We compare the single look performance with our baseline decision-level multi-look fusion approach and with a more refined hypothesis-level method. Evaluation of the multi-look approaches indicates that there are significant target identification performance benefits. In this presentation, we will discuss both hypothesis-level fusion, where we accumulate evidence not only over target type but also of target pose, thereby ensuring consistent interpretation across all the images; and feature-level fusion, where we accumulate evidence over parts of the model, thereby correctly accounting for model region visibility across the multiple views. Finally, we present the performance tradeoffs of the different multi-look approaches that we have evaluated so far, and discuss their benefits and limitations. The performance assessment is based on extensive analysis that uses multi-look SAR imagery covering a broad range vehicle types and operating conditions.