Traditional soil classification methods invoke physical differences based on particle size to group soils into textural classes. Resulting groupings are used to make predictions about soil attributes and processes of interest including hydrologic response. My hypothesis is that more useful classification schemes will be created by starting with response and applying an inverse approach to generate soil groupings. I propose an alternative classification scheme based on these hypotheses, using techniques of cluster analysis. The resulting system has high predictive capacity with simplicity comparable to the U.S. Dept. of Agriculture soil textural triangle or other similar classification diagrams. I conclude that: classification is most appropriate when carried out on process and objective specific bases; there is a physical meaning to cluster-based groupings, which allows for more appropriate segregation of response as compared to textural groupings; using clusters, a small number of samples can be used to characterize the range of response.