As our understanding of chemical processes increases, the complexity of the models developed to describe them also increases. In most cases the equations are nonlinear in the inputs and parameters, and must be solved numerically. At present in estimating parameters for large process models, there are two shortcomings in the existing knowledge about (multiresponse) parameter estimation. The first is, how effective is the present parameter estimation methodology when applied to large models, and the second is, can any advantage be gained from considering the parameter estimation problem as a whole. This paper will address these questions, by revisiting the various steps of a parameter estimation protocol. There is little discussion in the literature as to how all the steps for parameter estimation are related. In the development of this protocol all of the steps for parameter estimation will be revisited: parameter sensitivity analysis, statistical design of experiments, estimation of parameters and confidence regions. By considering all these steps as a whole the overall parameter estimation procedure can be more efficient and some pitfalls, such as local optima and incorrect confidence regions, may be dealt with in an appropriate manner. To illustrate the application of the protocol, two case studies related to polymerization models are presented. These case studies illustrate some of the problems that may be encountered in the parameter estimation process and how the proposed protocol can aid in overcoming them.132115132