We review existing methods of experimental design for discriminating between rival regression models. Box and Hill's algorithm and its extensions. and the work of Atkinson and Fedorov and Atkinson and Cox are particularly considered. The relationship between the various methods is pointed out. Several unrelated and less formal algorithms are also mentioned. The need for a multipurpose design strategy, which takes into account the need to design for estimation of the parameters of the correct model, is stressed.