Fast-match is a fast and effective algorithm for template matching. However, when matching colour images, the images are converted into greyscale images. The colour information is lost in this process, resulting in errors in areas with distinctive colours but similar greyscale values An improved fast-match algorithm that utilises all three RGB channels to construct colour sum-of-absolute-differences (CSAD) is proposed, thus improving the sum-of-absolute-differences distance used in fast-match. In this algorithm, each pixel in the image is categorised by clustering them using density-based spatial clustering of applications with noise (DBSCAN) algorithm over the RGB vector, then the number of pixels in each category and the cumulative RGB values for each RGB channel are calculated to identify the centroid of each category. The RGB vector centroid is used as the CSAD decision criteria, and inverse of number of pixels in each category is used as the differentiating coefficient to construct a new similarity measure. Experiment results demonstrate that this algorithm has significant higher accuracy for matching colour images than the original fast-match algorithm.