We present a detailed study on the classification of urban hyperspectral data with morphological profiles (MP). Although such a spectral-spatial classification approach may significantly increase achieved accuracy, the computational complexity as well as the increased dimensionality and redundancy of such data sets are potential drawbacks. This can be overcome by feature selection. Moreover it is useful to derive detailed information on the contribution of different components from MP to the classification accuracy by evaluating these subsets. We apply a wrapper approach for feature selection based on support vector machines (SVM) with sequential feature forward selection (FFS) search strategy to two hyperspectral data sets that contain the first principal components (PC) and various corresponding MP from an urban area. In doing so, we identify feature subsets of increasing size that perform best in terms of kappa for the given setup. Results clearly demonstrate that maximum classification accuracies are achieved already on small feature subsets with few morphological profiles.