Describing shape for purposes of analysis and characterization becomes more important each day as new images arrive and new imaging modalities are developed. Moreover, shape descriptors are crucial if ones goal is to compare shapes to relate them to some underlying process or as a clue to further evolution. The set of measurements called Minkowski Functionals (MFs), derived from integral geometry provides an excellent measure of shape that is at once simple yet informative. Better still, it can be shown that all shape descriptors (of reasonable objects) can be written as a linear combination of the d+1 MFs that apply to a d-dimensional object. I am much more interested in the application of the MFs than in their derivation; consequentially, this talk will focus on their usage and computation, and only tangentially on the mathematics from which they were derived. The concept of the MFs can be extended to describe the distribution of shapes, which leads to a new paradigm to describe and analyze ensembles.