In this paper, a feature extraction method for images is presented. It is based on the classical concept of Distance Transformation (DT) from which we develop a generalization: the Continuous Distance Transformation (CDT). Whereas the DT can only be applied to binary images, the CDT can be applied to both binary and gray-scale or color pictures. Furthermore, we define a number of new metrics and dissimilarity measures based on the CDT. Comparative experimental results are also presented for the new measures, using the NIST handwritten character database. Several experiments using the k-nearest neighbors classification rule are reported, with results that significantly improve the recognition rate of other measures like the Euclidean and some DT-based distances.