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We present a robust method for vehicle categorization in aerial images. This approach relies on a multiple-classifier system that merges the answers of classifiers applied at various camera angle incidences. The single classifiers are built by matching 3D-templates to the vehicle silhouettes with a local projection model that is compatible with the assumption of the little knowledge that we have of the viewing-condition parameters. We assess the validity of our approach on a challenging dataset of images captured in real-world conditions.