Uncertainties in the performance, emissions, and cost of advanced process technologies in early stages of development pose a challenge to process evaluation. Failure to account properly for uncertainties may result in misleading estimates that are then used for making decisions about technology selection and research planning. A new stochastic modeling capability has been developed for the ASPEN chemical process simulator that allows uncertainties in any process technology to be represented using probability distributions. This capability is briefly described. A detailed case study of an advanced integrated coal gasification combined cycle (IGCC) system is presented to illustrate the application of this capability. The case study involves identification and characterization of uncertainties in model inputs, propagation of uncertainties through the model, and interpretation of model results. The value of stochastic modeling is in the ability to evaluate the simultaneous effect of multiple uncertainties in a process technology and to develop more realistic estimates of process performance and cost than would otherwise be obtained from deterministic (point-estimate) analysis.