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Accurate land use/cover (LUC) classifications from satellite imagery are very important for eco-environment monitoring, land use planning and climatic change detection. Traditional statistical classifiers such as minimum distance (MD) have been used to extract LUC classifications in urban areas, but these classifiers rely on assumptions that may limit their utilities for many datasets. On the contrary, artificial neural network (ANN) and support vector machine (SVM) provide nonlinear and accurate ways to classify LUC from remote sensing images without having to rely on statistical assumptions. This article compared the results and accuracies of the ANN and SMV classifiers with the statistical MD classifier as a reference in extracting urban LUC from ETM+ images in Guangzhou, China. Results show that the overall accuracies of urban LUC classifications are approximately 96.03%, 94.71% and 77.79%, and the kappa coefficients reach 0.94, 0.92 and 0.67 for the ANN, SVM and MD classifiers, respectively, indicating that the ANN and SVM classifiers have greatly better accuracies than the traditional MD algorithm. It is concluded that while the ANN performs slightly better than the SVM, both ANN and SVM are effective algorithms in urban LUC extraction from ETM+ images because of their high accuracy and good performance.