To model combinatorial decision problems involving uncertainty and proba- bility, we introduce stochastic constraint programming. S tochastic constraint pro- grams contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best fea- tures of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochast ic constraint programs, and present a complete forward checking algorithm. Finally, we discuss a number of extensions of stochastic constraint programming to rela x various assumptions like the independence between stochastic variables, and compare stochastic con- straint programming with other approaches for decision making under uncertainty like Markov decision problems and influence diagrams.