In this paper, we propose a new clustering method based on the concept of maximum likelihood (ML) estimation. In general, the problem of local minima arises when we try to use the ML method in clustering problems. Our method circumvents this problem by employing the so called simulated annealing technique. In section 2, we formulate our clustering problem using the ML concept, and derive the ML estimation method. In section 3, validity of the derived method is confirmed by analyzing two artificial data and the famous Iris data. In the final section, our method is also extended from the viewpoint of sequential estimation.