The development of multi-modality image analysis has gained increasing popularity over recent years. Multi-modality image databases are being developed to benefit patient clinical care, research and education. The incorporation of histopathology in these multi-modality datasets is complicated by the large differences in image quality, content and spatial association. We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between non-structurally destructive and destructive imaging such that reliable registration and incorporation of three-dimensional (3D) histopathology can be achieved. We have developed registration algorithms to align the micro-CT, LIMA and histopathology data to a common coordinate system. Using this multi-modality image dataset we have developed a classification algorithm to identify on a pixel basis, the tissue types present. The output from the classification processing is a 3D color coded map of tissue distributions. The resulting complete dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture and cellular information in three dimensions. In this study we have chosen to use normal and diseased lung tissue, however the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue.