Image guided surgery has established in modern surgery rooms, enabling high technology support for complicated surgical interventions. The ability to exactly position surgical tools, even if the target of surgery is subsurface, relying just on pre-acquired image data, causes the great success of surgical navigation. In cerebral surgery, image guidance has a long tradition, even in orthopedics; recently it also appears to abdominal surgery. A major prerequisite for accurate position navigation is the careful mutual registration of patient-, tracking- A nd imaging-domains. Only intuitive and precise handling of the registration procedure leads to satisfying results. An easy to use and accurate registration method, integrating the iterative closest point (ICP) algorithm was developed and implemented as showcase in a Matlab(r) based tracking environment. Image data from a diagnostic scan are preprocessed by anisotropic diffusion filtering and reformatted to cubic voxels. The point sets for registration are extracted from the image volume and acquired by a tracked pointing device. Rough re-orientation of registration data is achieved by equalization of principal components. The ICP algorithm is applied to fully register both data sets. Accuracy of registration is quantified by distancemeasurements of the transformed tracking points from the surface and by measuring the summed distance of physical landmarks on the object's surface. The registration yields accurate overlay of the tracking and patient image domains, allowing exact navigation of surgical tools. The easy handling and accuracy of the developed registration method manifests the specific potential for clinical application.