A covariance matching approach for identifying errors-in-variables systems is analyzed for the general case. The asymptotic covariance matrix of the jointly estimated system parameters, noise variances and auxiliary parameters is derived. An algorithm for how to compute this covariance matrix from given system descriptions is also provided. The results generalize previous known special cases. Using Monte Carlo analysis, we illustrate the proposed algorithm. The results suggest close agreement between the theoretical and empirical accuracy. (C) 2014 Elsevier Ltd. All rights reserved.