This paper aims to estimate the parameters of a complex model representing an industrial scale polymerization process. The estimability analysis of the parameters prior to estimation allows simplifying the optimization problem but it is usually neglected in literature when industrial data is used for estimation. In this case, though, the estimability analysis would be even more important since usually less data is available, they are associated with a higher uncertainty and the experiments might not be designed as in laboratory or pilot plant. The orthogonalization method reduced from 68 to 29 the number of parameters of the model. Polymer properties, which are measured offline with low frequency, as well as process temperatures and flow rates are used for validating the model. Small deviations, up to 5%, between model prediction and experimental data indicate the quality of fit of the model and the importance of carrying out first an estimability analysis. (C) 2016 Elsevier Ltd. All rights reserved.