Advances in aerospace materials and the need to apply these materials to perform near their structural limits requires new approaches to accurately determine material composition and state, and most importantly, reliably predict service life. Unfortunately most Nondestructive Evaluation (NDE) procedures are manual in nature (even though the sensors employed may be sophisticated), particularly during the data interpretation phase. For large structures like rocket motors or aircraft fuselage elements, the amount of NDE data which must be examined to assure safety is enormous. Even with tools such as x-ray tomography, an inspector must intently study the reconstruction imagery using full concentration over long periods of time. Often problems or flaws must be identified which lie at the limits of geometrical resolution, density resolution or both. Attempts to automate this process have been frustrated by both the critical nature of the task (no machine-based approach has come close to earning confidence) and the difficulty in formulating sufficiently robust detection algorithms which account for the wide variety of manufacturing tolerances, yet maintain the specificity of a human observer without a large false alarm rate.