This paper describes work in the StatLog project comparing classification algorithms on large real-world problems. The algorithms compared were from symbolic learning (CART. C4.5, NewID, AC2,ITrule, Cal5, CN2), statistics (Naive Bayes, k-nearest neighbor, kernel density, linear discriminant, quadratic discriminant, logistic regression, projection pursuit, Bayesian networks), and neural networks (backpropagation, radial basis functions). Twelve datasets were used: five from image analysis, three from medicine, and two each from engineering and finance. We found that which algorithm performed best depended critically on the data set investigated. We therefore developed a set of data set descriptors to help decide which algorithms are suited to particular data sets. For example, data sets with extreme distributions (skew > l and kurtosis > 7) and with many binary/categorical attributes (>38%) tend to favor symbolic learning algorithms. We suggest how classification algorithms can be extended in a number of directions.