Contents: Introduction.- The Bayes Error.- Inequalities and Alternate Distance Measures.- Linear Discrimination.- Nearest Neighbor Rules.- Consistency.- Slow Rates of Convergence.- Error Estimation.- The Regular Histogram Rule. - Kernel Rules.- Consistency of the K-Nearest Neighbor Rule.- Vapnik-Chervonenkis Theory. - Combinatorial Aspects of Vapnik-Chervonenkis Theory.- Lower Bounds for Empirical Classifier Selection.- The Maximum Likelihood Principle.- Parametric Classification.- Generalized Linear Discrimination. - Complexity Regularization.- Condensed and Edited Nearest Neighbor Rules.- Tree Classifiers.- Data-Dependent Partitioning. - Splitting the Data.- The Resubstitution Estimate.- Deleted Estimates of the Error Probability.- Automatic Kernel Rules.- Automatic Nearest Neighbor Rules.- Hypercubes and Discrete Spaces.- Epsilon Entropy and Totally Bounded Sets.- Uniform Laws of Large Numbers.- Neural Networks.- Other Error Estimates.