We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.