Process simultalion relies heavily on the calculation of physical properties through thermodynamic models. The parameters in these models are subject to uncertainties that may, at first, appear to be small. However, the designs developed through use of these models can be significantly sensitive to these parameter uncertainties. Previous studies have considered special cases of design error or have involved simple sensitivity analyses close to the optimum design case. In the present work, we use Monte Carlo simulation with Latin Hypercube Sampling to quantify the probability of design errors. An example of binary distillation with phase-equilibrium calculations from a cubic equation of state is used to illustrate the technique.