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Content-Based Image Retrieval (CBIR) considers the characteristics of the image itself, for example its shapes, colors and textures. The Current approaches to CBIR differ in terms of which image features are extracted. Recent work deals with combination of distances or scores from different and independent representations. Content-based image retrieval has many application areas such as, education, commerce, military, searching, biomedicine and web image classification. This paper proposes a new image retrieval system, which uses color and Shape descriptions information to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the ycbcr color histogram which represents the global feature and Fourier descriptor as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard image databases such as Wang and UCID database. Experimental work show that the proposed approach improves the precision and recall of retrieval results compared to other approaches reported in literature.