This paper considers certain practical aspects of the identification of nonlinear empirical models for chemical process dynamics. The primary focus is the identification of second-order Volterra models using input sequences that offer the following three advantages: (1) they are "plant friendly;" (2) they simplify the required computations; (3) they can emphasize certain model parameters over others. To provide a quantitative basis for discussing the first of these advantages, this paper defines a friendliness index f that relates to the number of changes that occur in the sequence. For convenience, this paper also considers an additional nonlinear model structure: the Volterra-Laguerre model. To illustrate the practical utility of the input sequences considered here, second order Volterra and Volterra-Laguerre models are developed that approximate the dynamics of a first-principles model of methyl methacrylate polymerization. (C) 2001 Elsevier Science Ltd. All rights reserved.