This chapter presents advanced imaging and sensing technologies for NDE of polymer and polymer matrix-based composite materials and highlights how concepts such as artificial intelligence, data mining, and advanced computing can be used to improve our ability to identify damage in these materials. Recent advances in high-resolution imaging methods are generating large data sets relating to the failure of these materials under a wide variety of load conditions. Additionally, distributed sensor networks are often being incorporated into these materials for real-time inspection of structural components. Automating imaging and nondestructive evaluation (NDE) processes for polymer and composite materials and taking advantage of advances in artificial intelligence based processing methods is therefore critical to our ability to effectively use the results of these advanced testing infrastructure to understand the failure of existing materials systems and the design of new ones in the future. The chapter first describes the types of defects that need to be detected and analyzed. These defects can either be introduced during the fabrication of the composite materials or be created due to failure of the material during loading of the composite. Afterwards, several current NDE inspection methods for composite materials are described, in addition to opportunities to automate data processing for these inspections using artificial intelligence-based processing.