Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) have proven capabilities for non-cooperative target recognition (NCTR) applications. Both sensing modalities have been able to provide operational information in a robust manner. As processing power and communication capabilities on the battlefield increase, new opportunities for exploiting SAR/ISAR systems emerge. Multiple looks of the same target can now be used to increase performance and allow systems to operate at maximum ranges. The research presented in this work outlines a variety of methods for utilizing SAR/ISAR images in the presence of multiple looks. Specific accomplishments include: the development of an automated segmentation method to extract information from SAR images; the development of novel image fusion rules to integrate data from small numbers of independent SAR/ISAR systems; the development of a persistence framework to enhance target features in large, aspect-varying datasets; the development of a shadow classification technique to classify multiple targets based only on their shadow features in SAR imagery; and operational analyses were performed to determine how the algorithms would perform in realistic scenarios. The algorithms are tested on a variety of canonical and real-world datasets.