This article compares four widely utilized, yet fundamentally different, approaches for registering medical scans of the head. Comparisons are made on the basis of method, accuracy, robustness, computer requirements, and usability. This examination is intended to provide a means for determining an appropriate method for any given application. These approaches are: 1) an iterative method based on the repeated manual selection of 1-2 corresponding points, 2) an approach using the manual selection of 9-15 corresponding points, 3) an automatic surface matching method, and 4) an automatic approach based on voxel similarity. The methods are tested both on simulated data to provide a gold standard of accuracy, and on real data. All registrations are performed in the same visualization environment created for multipurpose image processing. Simulated data tests provided mean transformation errors and time requirements for the different methods, as well as the displacement errors for a set of anatomical landmarks. These results show all of the methods provide good accuracy when the data is not highly distorted and has a large amount of overlap. From the tests using real data both transformations and time requirements are tabulated for comparison. All of the techniques successfully aligned the real data with the exception of surface matching, which failed on the PET-MRI. Each method exhibits strengths and weaknesses that should be understood in order to utilize the most appropriate technique for a given problem. Based on the authors' examination, the voxel-similarity approach proved in general to be the method of choice.