This paper presents a model-based method to aid in the process validation for the purification of pharmaceutical drugs. The critical process parameters are identified by simulating process disturbances, and this information is then used to determine if the process control space is robust. Simulations are chosen to analyze the entire control space to also find nonlinearities and interaction effects between the process disturbances, which are used to determine where in the control space the critical quality attributes are the lowest, i.e., the worst case scenario. The real process conditions are estimated by running simulations according to plausible probability distributions using Latin hypercube sampling. The probability of batch failure can be estimated from this and it is shown that the worst case scenario is improbable for most cases. This information can help in planning validation experiments or determine which critical process parameters need a tighter control. Three case studies are used to illustrate the usefulness of the methods. It was found that the main critical process parameters in all three case Studies were variations in the modifier concentrations, for example, salt in ion-exchange chromatography and hydrophobic interaction chromatography, and the organic modifier in reversed-phase chromatography.