Retrieving visually similar images from image database needs high speed and accuracy. The researchers are investigating various text and content based image retrieval techniques to match the image features accurately. In this paper, a content-based image retrieval system (CBIR), which computes colour similarity among images, is presented. CBIR is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features. The colour is one important visual elements of an image. This document gives a brief description of a system developed for retrieving images similar to a query image from a large set of distinct images with histogram colour feature based on image index. Result from the histogram colour feature extraction, then using K-Means clustering to produce the image index. Image index used to compare to the histogram colour element of a query image and thus, the image database is sorted in decreasing order of similarity. The results obtained by the proposed system apparently confirm that partitioning of image objects helps in optimization retrieving of similar images from the database. The proposed CBIR method is compared with our previously existed methodologies and found better in the retrieval accuracy. The retrieval accuracy is comparatively good than previous works offered in CBIR system.