The observed features of a given phenomenon are not all equally informative : some may be noisy, others correlated or irrelevant. The purpose of feature selection is to select a set of features pertinent to a given task. This is a complex process, but it is an important issue in many fields. In neural networks, feature selection has been studied for the last ten years, using conventional and original methods. This paper is a review of neural network approaches to feature selection. We first briefly introduce baseline statistical methods used in regression and classification. We then describe families of methods which have been developed specifically for neural networks. Representative methods are then compared on different test problems.