Modelling multiple-input multiple-output petrochemical industrial dynamic systems is a complex task. Empirical models, based on linear state-space dynamic models often provide a sufficient degree of approximation in a statistically efficient way (i.e. with a small number of parameters). The use of subspace identification methods (SIM) proved to be an useful tool to estimate state-space model parameters since there is no need to specify the model structure prior to the model estimation task. However it is necessary to estimate the model's order and to select the proper inputs for each state-space model. In this article, it is presented a method based on the combination of bootstrapping and subspace identification techniques in order to quickly test many model alternatives in a very efficient way. The proposed method is an approximated approach that can be used to pre-select viable model alternatives (supported by the observed input-output data). © 2004 Elsevier B.V. All rights reserved.