Maximization of mutual information is a powerful method for registering images (and other data) captured with different sensors or under varying conditions, since the technique is robust to variations in the image formation process. On the other hand, the high level of robustness allows false positives when matching over a large search space and also makes it difficult to formulate an efficient search strategy for this case. We describe techniques to overcome these problems by aligning image entropies, which are robust to illumination variation and can be applied to multi-sensor registration. This results in a lower rate of false positives and a more efficient method to search an image for the matching position. The techniques are applied to real imagery and compared to methods based on mutual information and gradients to demonstrate their effectiveness.