In this paper, separated feature indexing was used for region-based image retrieval. In the beginning, three methods, such as K-means, CA-Clustering and Blobworld, were used for region generation in this paper. Comparing their results, the retrieval results using Blobworld perform better than the other two. Therefore, Blobworld is chosen to generate regions in the following experiments. After generating a few regions in an image, in this paper, a few heterogeneous features, such as color signature, moments, contrast, anisotropy, edge orientation histogram, edge density, perimeter, area, position, etc. were used for region matching. According to the variations of two or more query regions, the weights to each feature were automatically generated. Furthermore, the individual indexing was built and maintained for each heterogeneous feature, i.e., separated feature indexing. Once the retrieval results from separated feature indexing were obtained, two processes about normalization and combining distances from these heterogeneous features should be completed in order to achieve the final retrieval result. In our experiments, two functions were tested for normalizing heterogeneous distances and four functions for combining the normalized distances into a global measure. The experimental results show that the best result can be obtained by using the one based on norm1 and mean2 operations. We used the precision/recall and the average normalized modified retrieval rank (ANMRR) to evaluate the experiments in this paper.