||To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building body-wide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. The proposed approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax and 32 abdominal data sets and some 25 organs defined in them, the hierarchical approach to modeling seems to effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients. Although most of this presentation will focus on model building, other aspects of the larger QR project including object recognition, delineation, and real time performance will also be touched upon.