As more remotely sensed data becomes available there is an increasing need for automated image processing techniques. In particular there is a need for the selection of relevant attributes used in a given classifcation problem. Neural networks are widely used for classifcation of image data, but few practitioners achieve optimal results. In part, this is due to the use of noisy or irrelevant data. This paper compares a new attribute selection method specifcally for use with neural networks, namely contribution analysis, with the more general wrapper method of attribute selection.