A method is defined and tested for the characterization of agricultural land from multi-spectral imagery, based on singular value decomposition (SVD) and key vector analysis. The SVD technique, which bears a close resemblance to multivariate statistic techniques, has previously been successfully applied to problems of signal extraction for marine data and forestry species classification. In this study the SVD technique is used as a classifier for agricultural regions, using airborne Daedalus ATM data, with 1 m resolution. The specific region chosen is an experimental research farm in Bavaria, Germany. This farm has a large number of crops, within a very small region and hence is not amenable to existing techniques. There are a number of other significant factors which render existing techniques such as the maximum likelihood algorithm less suitable for this area. These include a very dynamic terrain and tessellated pattern soil differences, which together cause large variations in the growth characteristics of the crops. The SVD technique is applied to this data set using a multi-stage classification approach, removing unwanted land-cover classes one step at a time. Typical classification accuracy's for SVD are of the order of 85-100%. Preliminary results indicate that it is a fast and efficient classifier with the ability to differentiate between crop types such as wheat, rye, potatoes and clover. The results of characterizing 3 sub-classes of Winter Wheat are also shown.