In this paper, we present an approach to retrieve documents based on logo spotting and recognition. A document retrieval system is proposed inspired from our previous method for logo spotting and recognition. First, the key-points from both the query logo images and a given set of document images are extracted and described by SIFT descriptor, and are matched in the SIFT feature space. They are filtered by the nearest neighbor matching rule based on the two nearest neighbors and are then post-filtered with BRIEF descriptor. Secondly, logo segmentation is performed using spatial density-based clustering, and homography is used to filter the matched key-points as a postprocessing. Finally, for ranking, we use two measures which are calculated based on the number of matched key-points. Tested on a well-known benchmark database of real world documents containing logos Tobacco-800, our approach achieves better performance than the state-of-the-art methods.