Methodologies to determine the Design Space of a pharmaceutical product, within which continuous improvement can be implemented and postapproval changes in material attributes and process parameters can be introduced without prior approval, are presented. The type of methodology used depends on the type of experimental data obtained for the purpose of determining the Design Space. However, when one is unsure about the data collected to determine the Design Space, one can determine the quality of the data from successive application of the various methods and evaluation of the desirability measure for each method. The Design Spaces for a two- and three-process tablet manufacturing are determined by Response Surface Method (RSM), Bayesian Post Predictive Approach (BPPA), and Artificial Neural Networks (ANN), based on local or global specification limits of the response variables provided by multiresponse optimization and overlapping responses, respectively. The Response Surface Method is the most effective of these methods in determining the Design Spaces for the aforementioned data sets, confirming that the particular data are complete and lack uncertainty or structure, the specific features that the Bayesian Post Predictive Approach and Artificial Neural Networks methods are suited to address, respectively.