Human computer models represent a useful tool for investigating the human body response to external static/dynamic loads or for human-centred design. Articulated Total Body (ATB) models are the simplest human multibody models, where body segments are represented by ellipsoids joined at skeletal articulations. Over the years, regression models on both living subjects’ and cadavers’ data have been developed to predict body segments properties. These models are affected by two main limitations: the only inputs are the subject’s weight and height, not considering that for the same combination different morphologies can exist; secondly, regression analyses were performed over a specific population not including peculiar morphologies (under-weight or obese). A novel methodology for developing anthropomorphic ATB models is here presented: a statistical shape model able to predict the external geometry of the human body from a limited set of anthropometric measurements was implemented and body segments were obtained by segmentation; the respective inertial properties were computed from volumes, assuming a constant density value. The properties of this new anthropomorphic ATB model were compared to those calculated by GEBOD (Generator of Body Data), a well-known programme for ATB data calculation. A virtual population of twenty subjects was analysed: with reference to the inertial properties the most relevant differences occurred at the abdomen and the thighs segments (60% relative error), while the trunk, the shoulder and the calves represent the most critical areas for the geometry reconstruction (50 mm average error). The significance of these outcomes was investigated performing multibody simulations with various scenarios.@en