Accurate and robust object classification is an unsolved problem in the field of digital radiographic image (DRI) analysis. The problem is confounded by variations in objects’ scale, rotation, translation, point-of-view, partial obscuration, and noise. In addition, DRIs present a unique set of challenges, since they reveal the internal structures and features of objects rather than the surface features revealed in most other types of images. The objective of this research is to design, implement, and test a general classifier for DRIs that is robust in the presence of occlusions and noise. To achieve this objective, the proposed ALISA Component Module will use a generalized two-tier analysis token to extract feature vectors associated with the pixel at the center of the analysis token. The feature vectors are accumulated in histograms during training, and classification is performed by comparing feature vectors generated from test images to trained histograms. Formal experiments conducted with the Component Module operating on both canonical shapes and real-world applications using DRIs demonstrated robust classification performance. The Experiments have also demonstrated that the Component Module can learn to classify components of objects from small training sets, as well as effectively classify similar components independent of their position and some variation in their orientations. The Component Module is also robust to uniform and non-uniform occlusions, and noise.