Multimodality images obtained from medical imaging systems such as computed tomography (CT), magnetic resonance (MR) imaging, positron emission tomography (PET), and single photon emission computed tomography (SPECT), generally provide complementary characteristic and diagnostic information. Synthesis of these image data sets into a single composite image containing these complementary attributes in accurate registration and congruence would provide truly synergistic information about the object(s) under examination. We have developed a new method which produces such correlation using parametric Chamfer matching. The method is fast, accurate, and reproducible. Surfaces ar initially extracted from two different images to be matched using semi-automatic segmentation techniques. These surfaces are represented as contours with common features to be matched. A distance transformation is performed for one surface image, and a cost function for the matching process is developed using the distance image. The geometric transformation includes three- dimensional translation, rotation, and scaling to accommodate images of different position, orientation, and size. The matching process involves searching this multi-parameter space to find the best fit which minimizes the cost function. The local minima problem is addressed by using a large number of starting points. A pyramid multi-resolution approach is employed to speed up both the distance transformation and the multi-parameter minimization processes. Robustness in noise handling is accomplished using multiple thresholds embedded in the multi- resolution search. The algorithm can register both partially overlapped and fragmented surfaces. Manual intervention is generally not necessary. Preliminary results suggest registration accuracy on the order of the voxel size used in the registration process. Computational time scales with the number of matching elements used, with about five minutes typical for 2563 images using a modern desktop workstation.