In this paper, a new multitarget tracking approach is proposed. In the proposed approach, a non-iterative fuzzy clustering means algorithm is used to generate the association measures between the received measurements and the targets. Measurements-to-tracks associations are computed jointly across all targets and all validated measurements using the non- iterative fuzzy clustering means algorithm. For a given target, the validated measurement that has the maximum fuzzy association weight is used for updating the state of the target. The performance of the proposed approach is evaluated and compared to that of the standard nearest-neighbor association and conventional fuzzy logic data association approaches. The results show that the proposed tracking approach achieves better performance compared to the standard tracking approach and the conventional fuzzy tracking approaches.