Comparison of shapes is at best a difficult problem. Although many methods of measuring shapes are available, such as circularity, Fourier descriptors, and invariant moments, these methods generally suffer from one or more of the following drawbacks, (1) requiring previous segmentation of the shape, (2) inability to relate the metric intuitively to the shape, and (3) inability to describe local features of object shape. We describe two new metrics based on cores: the average chamfer distance and the average fractional difference. These metrics do not require prior segmentation of objects, can be used to describe local features of object shape, and are intuitively related to degree of shape similarity or dissimilarity. Furthermore, we demonstrate that these metrics are well-behaved, producing output that varies in a predictable fashion, increasing in value as shapes become increasingly different and decreasing in value as shapes become increasing similar.