In this paper, we present an exploratory factor analytic approach to morphometry in which a high-dimensional set of shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to the morphometric investigation. The analysis is based on information about size difference between the differential volume about points in a template image and their corresponding volumes in a subject image, where the correspondence is established by non-rigid registration. The Jacobian determinant field of the registration transformation is modeled by a reduced set of factors, whose cardinality is determined by an algorithm that iteratively eliminates factors that are not informative. The results show the method’s ability to identify gender-related morphological differences without supervision.