Multimodality images obtained from different medical imaging systems such as magnetic resonance (MR), computed tomography (CT), ultrasound (US), positron emission tomography (PET), single photon emission computed tomography (SPECT) provide largely complementary characteristic or diagnostic information. Therefore, it is an important research objective to `fuse' or combine this complementary data into a composite form which would provide synergistic information about the objects under examination. An important first step in the use of complementary fused images is 3D image registration, where multi-modality images are brought into spatial alignment so that the point-to-point correspondence between image data sets is known. Current research in the field of multimodality image registration has resulted in the development and implementation of several different registration algorithms, each with its own set of requirements and parameters. Our research has focused on the development of a general paradigm for measuring, evaluating and comparing the performance of different registration algorithms. Rather than evaluating the results of one algorithm under a specific set of conditions, we suggest a general approach to validation using simulation experiments, where the exact spatial relationship between data sets is known, along with phantom data, to characterize the behavior of an algorithm via a set of quantitative image measurements. This behavior may then be related to the algorithm's performance with real patient data, where the exact spatial relationship between multimodality images is unknown. Current results indicate that our approach is general enough to apply to several different registration algorithms. Our methods are useful for understanding the different sources of registration error and for comparing the results between different algorithms.