Robust control theory is widely used as the theoretical basis for the design of controllers with reduced sensibility to model errors. The model parameters variance-covariance (VC) matrix allows to design controllers with a consistent control action, even in the presence of moderate model mismatch. This paper presents a technique to extract the state-space model variance-covariance matrix using bootstrap techniques. The VC matrix is estimated from bootstrapped models using a first-order approximation of the model parameters space. The technique is applied by estimating the nominal model uncertainty of a deisopentanizer petrochemical unit. The model uncertainty is determined more accurately by the proposed method, when compared to the use of minimal canonical parameterization, providing better first-order approximation confidence intervals. © 2006 Elsevier Ltd. All rights reserved.