The significance of several key multi-frequency, polarimetric back-scatter parameters extracted from calibrated and noise-corrected NASA/JPL DC-8 SAR data are examined. The data were collected during the 1989 MAESTRO-1 campaign over the Flevoland agricultural test site. Calibration uncertainty estimates are used to specify minimum separations between features. Thirteen different backscatter types were identified from the test site data, including eleven different crops, one forest and one water area. Using the parameters with the highest separation for a given class, a hierarchical algorithm was developed to classify the entire image. All three frequencies and all polarizations were used to construct the rules for the classifier. Results indicate that multi-frequency, polarimetric radar backscatter signatures can be useful in classifying several different ground cover types in agricultural areas.