An artificial neural network has been applied for the estimation of catalytic performance by taking, as an example, the selectivities of various products in the oxidative dehydrogenation of ethylbenzene on a series of promoted and unpromoted SnO2 catalysts. The selectivities estimated by a leave-one-out test agreed surprisingly well with the experimentally observed values, indicating the possibilities of a neural network as a tool for catalyst development.