We present a method for faithfully approximating the Hunt94, LLAB and RLAB color appearance models by means of feed-forward neural networks trained with the error back-propagation algorithm. In particular we present experimental evidence that in eight “standard” viewing conditions the same network architecture is capable of learning quite satisfactorily the transformations performed by the three models.