There is a growing research interest in reliable content based image retrieval (CBIR) methods for biomedical images. Feature representation algorithms used in indexing medical images have to address conflicting goals of reducing feature dimensionality while retaining important and often subtle biomedical features. In a collection of digitized X-rays of the spine, for example, a typical user is interested in only a small region of the vertebral body pertinent to the pathology: for this experiment, the Anterior Osteophyte (AO). To address this, we have proposed the use of partial shape matching (PSM) methods for spinal X-ray image retrieval. This paper describes an evaluation of a Procrustes distance based PSM method for pathology-based retrieval. It was conducted on a subset of data selected from a collection of 17,000 digitized X-rays of the spine from the Second National Health and Nutritional Examination Survey (NHANES II) maintained at the U.S. National Library of Medicine. Two classifications for AO were used to evaluate the PSM algorithm, viz., Macnab classification, which is a published method, and a grading system developed by us. The evaluation based on ground truth established by the classifications shows that the PSM algorithm is a promising approach for content-based image retrieval of biomedical images.