A dynamic system with both input and output measurement errors is termed as an errors-in-variables (EIV) system. Employment of traditional identification methods for EIV systems will result in biased parameter estimates. The existing methods of EIV identification like Bias Eliminated Least Squares (BELS), and Subspace EIV methods may not always yield good parameter estimates. To address these open issues, this paper presents a maximum-likelihood (ML) approach using the Expectation Maximization (EM) algorithm. The efficacy of the proposed method is demonstrated with an experimental study.