Calculation of the measurement error covariance matrix is an essential requirement in data reconciliation methods. It is common practice to assume that the measurement errors are normal and have a known covariance matrix. A new robust method of measurement error covariance matrix calculation is presented. This approach directly treats the measured process variables but uses an M-estimator to reject the outliers and tunes the measured values for deviations from steady-state.