Simulated annealing is a probabilistic method proposed in Kirkpatrick, Gelett and Vecchi (1983) and Cerny (1985) for finding the global minimum of a cost function that may possess several local minima. It works by emulating the physical process whereby a solid is slowly cooled so that when eventually its structure is "frozen," this happens at a minimum energy configuration. We restrict ourselves to the case of a cost function defined on a finite set. Extensions of simulated annealing to the case of functions defined on continuous sets have also been introduced in the literature (e.g., Geman and Hwang, 1986; Gidas, 1985a; Holley, Kusuoka and Stroock, 1989; Jeng and Woods, 1990; Kushner, 1985). Our goal in this review is to describe the method, its convergence and its behavior in applications.