Identification of systems with slowly sampled output is studied. A linear parameter varying (LPV) model with multi-model structure is used to solve the problem. The output error (OE) method is used to estimate model parameters. Firstly, the local models and weighting functions are estimated separately using optimization methods. Then, a relaxation iteration method is developed to refine the parameters of the total model. For LPV model structure determination, an engineering approach is proposed that combines process knowledge with the so-called final output error criteria (FOE). The method is verified using both simulation data and industrial data. In the industrial case study, the LPV models give more accurate prediction of product qualities than that of a linear dynamic model and that of a static nonlinear model; the result also indicates the necessity of using test signals in soft-sensor development. (c) 2018 Elsevier Ltd. All rights reserved.