I recent years, much attention has been devoted to the development of metaheuristics and local search algorithms for tackling stochastic combinatorial optimization problems. This paper focuses on local search algorithms; their effectiveness is greatly determined by the evaluation procedure that is used to select the best of several solutions in the presence of uncertainty. In this paper, we propose an effective evaluation procedure that makes use of empirical estimation techniques. We illustrate this approach and we assess its performance on the probabilistic traveling salesman problem. Experimental results on a large set of instances show that the proposed approach can lead to a very fast and highly effective local search algorithm.