We present here, an image description approach based on prosemantic features. The images are firstly represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. In this paper we will show how prosemantic features outperform traditional low-level features in a variety of tasks. One is content-based retrieval: we included prosemantic features into the framework of the QuickLook² image retrieval system. Target search experiments show that the use of prosemantic features, combined with the relevance feedback mechanism of QuickLook², allows for a more successful and quick retrieval of the query images with respect to low-level features. Moreover, we will show the effectiveness of our features for the browsing and visualization of the results obtained from image search engines.