A number of image registration algorithms are currently used to register both intra-modality and inter-modality image data sets. These algorithms, although tested, have not always been optimized or evaluated in relationship to other registration algorithms available. Analysis of registration algorithms is often difficult, due to the fact that the registration components are integrated with each other and cannot be easily isolated form the rest of the algorithm. This study outlines some important features of registration algorithms and describes a method by which these algorithms may be analyzed. This method was used to analyze (1) two cost functions, a normalized standard deviation function and a conditional entropy function, (2) two search strategies, Powell's method and a derivative method, and (3) a number of iterative relaxation/data reduction techniques. These registration components were tested with a number of intra- modal and inter-modal data sets. The cost function analyses suggest that segmentation is necessary for some inter-modal registration problems. Multi-resolution techniques are also discussed with regard to registration effectiveness. A Euclidean distance measure was used to conditional entropy combined with a derivative search strategy may be the most robust approach to image registration, whereas the normalized standard deviation with Powell's search strategy produces the worst results. Generalization and further work are indicate in the discussion.