When building mathematical models to represent chemical phenomena, the identification of the system kinetics can be one of the most difficult and time-consuming processes. The high-parameter correlations, which are typical of the most common reaction networks (parallel, consecutive reactions, etc.), often make the identification and proper estimation of the model parameters extremely difficult. For this reason, a novel approach to model-based experiment design able to yield optimally informative experiments while simultaneously reducing the correlations between the model parameters was proposed in a previous publication. (Franceschini and Macchietto, AIChE J. 2008;54:109-1024) This method was demonstrated very effective when applied to a simple, illustrative case. This article investigates this novel approach in more details and presents an algorithm and a structured set of recommendations to guide a user ill the formulation of the design sequence most appropriate to the features of the problem under investigation. The application to a more complex fermentation example is used to demonstrate the usefulness of the proposed algorithm, the effectiveness of the novel criteria in yielding precise parameter estimates as less correlated as possible, and the suitability of this method to various design procedures (sequential, parallel, etc.). (C) 2008 American Institute of Chemical Engineers AIChE J, 54: 3221-3238, 2008