Device-independent color imaging demands a reliable color-appearance model. We present a method for faithfully approximating 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 several "standard" viewing conditions recommended for testing color-appearance models, the same network architecture is capable of learning quite satisfactorily the transformations performed by different color-appearance models.