A method for black-box identification of uncertain systems is presented. The method identifies a nominal model and an uncertainty model set, consisting of unfalsified uncertainty models. Minimisation of a Chebyshev criterion leads to computationally favourable linear programming problems and allows the possibility to include a priori information in the form of linear constraints without making the computations more complex. Using data compression via correlation computations solves the computation problem associated with identifying unfalsified uncertainty models. The application of set-valued uncertainty models to robust process control is illustrated in a simulation study of robust model predictive control of a distillation column. (C) 2001 Elsevier Science Ltd. All rights reserved.