Neural networks have received a great deal of attention over the last few years. They are being used in the area of prediction and classification, areas where regression models and other related statistical techniques have traditionally been used. There has been much publicity about the ability of artificial neural networks to learn and generalize. In fact, the most commonly used artificial neural networks, called multilayer perceptrons, are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software. This paper explains what neural networks are, translates neural network terminology into statistical terminology, and shows the relationships between neural networks and statistical models such as generalized linear models, maximum redundancy analysis, projection pursuit, and cluster analysis. Neural networks and statistics are not competing methodologies for data analysis. There is considerable overlap between the two fields. Neural networks include several models such as MLPs that are useful for statistical applications. Statistical methodology is directly applicable to neural networks in a variety of ways, including estimation criteria, optimization algorithms, confidence intervals, diagnostics, and graphical methods. Better communication between the fields of statistics and neural networks would benefit both.