Performances of estimators of the linear model under different level of autocorrelation (ρ) are known to be affected by different specifications of regressors. The robustness of some methods of parameter estimation of linear model to autocorrelation are examined when stochastic regressors are normally distributed. Monte Carlo experiments were conducted at both low and high replications. Comparison and preference of estimator(s) are based on their performances via bias, absolute bias, variance and more importantly the mean squared error of the estimated parameters of the model. Results show that the performances of the estimators improve with increased replication. In estimating all the parameters of the model, the Ordinary Least Square (OLS) estimator is more efficient than any of the Generalized Least Square (GLS) estimators considered when − 0.25 < ρ ≤ 0.25; and the Maximum Likelihood (ML) and the Hildreth and LU (HILU) estimators are robust.