An optimal experiment design assumes the existence of an initial or nominal process model. The efficiency of this procedure depends on how the initial model is chosen. This creates a practical dilemma as estimating the model is precisely what the experiment tries to achieve. A novel approach to experiment design for identification of nonlinear systems is developed, with the purpose of reducing the influence of poor initial values. The experiment design and the parameter estimation are conducted iteratively under a receding-horizon framework. By taking steady-state prior knowledge into account, constraints on the parameters can be derived. Such constraints help reduce influence of poor initial models. The proposed algorithm is illustrated through examples to demonstrate its efficiency. (C) 2010 American Institute of Chemical Engineers AIChE J. 57: 2808-2820,2011