A new gray-box method for nonlinear process identification is presented. Industrial deployment for model predictive control (MPC) is the primary focus of this development. For flexibility, the identification accommodates Hammerstein, Wiener and the more general N-L-N block-oriented structures. Instruments comprised of linear and nonlinear combinations of inputs and outputs are also accommodated. Unique to this approach is the utilization of two sets of bases. One is constructed using an estimate of the process poles and the other is constructed using a predefined set of special cubic splines. An intriguing aspect of this formulation is that nonlinear dynamics are implicitly accommodated. In addition, problems associated with identifying the linear portion of the model in conventional block oriented formulations are removed. Because of the bases formulation, it is possible to solve the identification problem for many supported structures by convex optimization and hence avoid the inherent problems of iterative solutions. To insure open-loop unbiased estimates, any structures using output nonlinearities do require an iterative solution. Two test cases from the open literature are presented as are results from plant step-test data on a problematic air separation unit. (C) 2011 Elsevier Ltd. All rights reserved.