In many applications, fusion of images acquired via two or more sensors requires image alignment to an identical pose, a process called image registration. Image registration methods select a sequence of transformations to maximize an image similarity measure. Recently a new class of entropic-graph similarity measures was introduced for image registration, feature clustering and classification. This chapter provides an overview of entropic graphs in image registration and demonstrates their performance advantages relative to conventional similarity measures. In this chapter we introduce : techniques to extend image registration to higher dimension feature spaces using Rényi's generalized «-entropy. The «-entropy is estimated directly through continuous quasi additive power weighted graphs such as the minimal spanning tree (MST) and k-Nearest Neighbor graph (kNN). Entropic graph methods are further used to approximate similarity measures like the « mutual information, «-Jensen divergence, Henze-Penrose affinity and Geometric-Arithmetic mean affinity. These similarity measures offer robust registration benefits in a multisensor environment. Higher dimensional features used for this work include basis functions like multidimensional wavelets and independent component analysis (ICA). Registration is performed on a database of multisensor satellite images. Lastly, we demonstrate the sensitivity of our approach by matching local image regions in a multimodal medical imaging example.