The general problem of developing algorithms for the automated or computer-assisted indexing of images by structural contents is a significant research challenge. This is particularly so in the case of biomedical images, where the structures of interest are commonly irregular, overlapping, and partially occluded. Examples are the images created by digitizing film x-rays of the human cervical and lumbar spines. We have begun work toward the indexing of 17,000 such spine images for features of interest in the osteoarthritis and vertebral morphometry research communities. This work requires the segmentation of the images into vertebral structures with sufficient accuracy to distinguish pathology on the basis of shape, labeling of the segmented structures by proper anatomical name, and classification of the segmented, labeled structures into groups corresponding to high level semantic features of interest, using training data provided by biomedical experts. In this paper, we provide a technical characterization of the cervical spine images and the biomedical features of interest, describe the evolving technical approach for the segmentation and indexing problem, and provide results of algorithms to acquire basic landmark data and localization of spine regions in the images.