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Given a broad set of content-based audio features, we employ principal component analysis for the composition of an optimal feature set for environmental sounds. We select features based on quantitative data analysis (factor analysis) and conduct retrieval experiments to evaluate the quality of the feature combinations. Retrieval results show that statistical data analysis gives useful hints for feature selection. The experiments show the importance of feature selection in environmental sound recognition.