Synthesis capabilities based on conventional mathematical programming techniques such as MINLP algorithms can pose certain difficulties, especially with black-box, sequential modular simulators (SMS). Further, since commercial simulators are based on a deterministic framework, synthesis of processes under uncertainty is not possible. This paper presents a generalized synthesis capability built around the public version of the ASPEN Process Simulator based on stochastic annealing - a new algorithm for synthesis under uncertainty. It is complementary to MINLP synthesis capabilities in ASPEN, and provides an automated approach to stochastic synthesis. Further, it achieves the trade-off between accuracy and computational efficiency, by selecting the optimal number of samples. The implementation of this new synthesis capability in ASPEN is illustrated by synthesizing a bench-mark process in chemical synthesis - the HDA process. The results show that stochastic annealing, which overcomes many of the problems associated with a MINLP approach in a simulation environment, can be a potential tool for stochastic synthesis with deterministic, black-box simulators.