Unsupervised clustering is a powerful technique that can be used for distinguishing the real target from the false targets such as flares in the images formed by infrared sensors in missiles. The rosette scan image is formed by a single infrared sensor scanning the total field of view by rosette pattern. This image is a greyscale image in which the real target could be differentiated from flares regarding the intensity of radiation sensed by IR sensor or the spatial position of the target. The ISODATA is one of the unsupervised clustering algorithms that could be used in infrared guided missiles detectors, since the algorithm itself computes the number of clusters or in other words the number of flares. Although the only drawback of the ISODATA is the extension in the processing time of the algorithm while the missile approaches the target and the number of detected clusters varies frequently, we can still take advantage of the algorithm by speeding up the most time consuming parts. In our approach to identify and locate the time consuming parts of the algorithm, first a profiling on a software implementation of the ISODATA algorithm has been carried out. The results show that over 60 percent of the complete execution time of the algorithm is consumed in computation of the distance from cluster centres. In this paper we propose a pipelined and parallelized datapath for hardware implementation of the algorithm in order to speed up the distance computation process and overcome the problem