This paper presents the "Volume-Matcher 3D" project, an approach for a data-driven comparison and registration of three-dimensional (3D) images. The approach is based on a neural network model derived from self-organizing maps and extended in order to match a full 3D data set of a "source volume" with the 3D data set of a "target volume." The method developed has been tested on real cases of interest in medical imaging. The results have been evaluated on the basis of both an objective mathematical function and visual analysis performed by an expert. The software was implemented on a high performance PC using AVS/ExpressTM.