Random Graphs for Statistical Pattern Recognition describes several classes of random graphs used in pattern recognition. It covers the neighborhood graphs introduced by Toussaint, as well as the various generalizations and specific cases. These graphs have been widely used for clustering. A newly introduced random graph, called the class cover catch digraph (CCD), is the primary focus of the book. The properties of the CCCD are investigated, along with applications to discrimination, dimensionality reduction, and aggregation/association detection.