This paper addresses the image retrieval problem for online image databases. Solutions to this problem may find applications in many areas including online information analysis, multimedia information retrieval and Web applications. In this research, we investigate this problem by proposing a novel solution, called CLEAR, that incorporates multi-features including color, texture, shape, as well as conventional geometric information. Moreover, CLEAR extracts the features based on regions in an image as opposed to the whole image domain, which allows the features to be more descriptive in indexing the objects of an image. To address the “inaccuracy” problem typically existing in many color-feature based retrieval systems, fuzzy logic is applied to the traditional color histogram to develop a fuzzy color histogram as the color feature vector. A similarity function is defined based on the multi-features through a balanced combination between global and regional similarity measures. In order to further improve the retrieval efficiency, a secondary clustering technique is developed and employed in CLEAR to significantly save query processing time without compromising retrieval precision. An implemented prototype system of CLEAR has demonstrated a promising retrieval performance for a test database containing 2000 general-purpose color images, as compared with its peer systems in the literature.