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Object-oriented classification is a useful tool for analysis of high-resolution imagery due to the incorporation of spectral, textural and contextual variables. However, feature selection and incorporation of appropriate training sites can be difficult. We compared two object-oriented image classification approaches, one using a decision tree (DT), the other a nearest neighbor classification (NN) with regard to classification accuracy, effort involved and feasibility for mapping similar areas. We used a QuickBird satellite image to map and rangeland vegetation in a 1200 ha pasture in southern New Mexico. In the DT approach, we used ground truth data from plots (8.75 m(2)) as input for a decision tree to create a rule base for classification. In the NN approach, larger polygons (mean=100 m(2)) served as training areas for a nearest neighbor classification. Overall accuracy was 80% using the DT and 77% using the NN classification. The DT was a superior tool for reducing the number of input features, but this technique required more field data, export to a decision tree program and was more time consuming. With the NN approach, input features were selected within the image analysis program and were applied to the classification immediately. The use of larger polygons for training and test samples was more appropriate for use in an object-oriented environment than the small plots. We concluded that for arid rangeland classification from QuickBird data, the NN technique required less time in the field and for image analysis, had comparable accuracy to the DT approach, and would be appropriate for mapping similar areas. A combination of both methods would incorporate the advantages of feature selection in a DT with the object-oriented nature of the analysis.