Supervised maximum likelihood classification was compared with a supervised binary decision tree for crop classification from multitemporal LANDSAT MSS data. Similar levels of classification accuracy were obtained using both algorithms, but the ease of training and computational simplicity of the binary decision tree suggest that this algorithm may be a viable alternative to the maximum likelihood for the analysis of data sets with high dimensionality such as multitemporal LANDSAT MSS data.