Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis we present a region-based image retrieval system that uses color and texture as visual features to describe the content of an image region. Our contribution is of three directions. First, we use Gabor filters to extract texture features from arbitrary shaped regions separated from an image after segmentation to increase the system effectiveness. Second, to speed up retrieval and similarity computation, the database images are segmented and the extracted regions are clustered according to their feature vectors using Self Organizing Map (SOM) algorithm. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using Gabor filters and color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a 1000 COREL color image database. From the experimental results, it is evident that our system performs significantly better and faster compared with other existing systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image, and features extracted from some image regions. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them gives better retrieval results for almost all semantic classes.