Content-Based Image Retrieval (CBIR) has received a comprehensive attention from researchers due to the quickly growing and the diffusion of image data-bases. Despite the huge research efforts consumed for CBIR, the completely promising results have not yet been presented. In this paper, a novel weighted multi-feature voting technique is proposed which incorporates various types of low-level visual features such as texture, shape and color in retrieval process. The color feature is described by color histogram and hierarchical annular his-togram whereas shape feature is described by edge histogram and edge direc-tion histogram while texture feature is described by gabor filter and co-occurrence matrix. Each feature has certain weight computed based on its pre-cision to reflect its importance in retrieval procedure. Furthermore, different distance measures are implemented to get the highest precision of each fea-ture. The results indicate that by applying multi-features and multi-distance measures, the obtained retrieval system outperforms other existing methods with accuracy 89.5% for Wang database, 91.5% for Caltech101 database and 89% for UW database.