For decades, broadband sonar has been a key tool in the underwater detection and classification of man-made targets. The DoD’s recent focus to clean up discarded WWII-era munitions has pushed a flurry of research and advancements in the areas of target scattering, environmental noise modeling, signal processing, and automatic target recognition (ATR). It is the interdisciplinary nature of this problem that makes it incredibly challenging, namely, the fact that the ability to classify an object is heavily influenced by the operational choices (grazing angle and vehicle altitude), surrounding environmental factors (target type, burial state, sea state, and bottom topography), and the processing tools (data products, feature-set, and ATR training/testing). This talk will outline the key steps underlying the process of trying to detect and classify underwater unexploded ordnance (UXO). It begins by pushing raw time domain data, which can be generated either experimentally or via models, through a set of signal processing tools to create data products (acoustic color and SAS images). These data products are fed through feature extraction processes, followed by ATR algorithms, in order to arrive at the final classification of an object. Examples to be presented include objects of interest to the remediation of underwater UXO. [Research supported by SERDP and ONR.].