Abstract: Assumptions in classical linear regression model that regressors are assumed to be independent and non-stochastic in repeated sampling are often violated by economist and other social scientists. This is because their regressors are generated by stochastic process beyond their control. Consequently, in this study we examine the performances of the Ordinary Least Square (OLS) and four Generalized Least Square (GLS) estimators of linear model with autocorrelated error terms when normally distributed stochastic regressors exhibit multicollinearity. These estimators are compared by examing their finite sampling properties at various levels of autocorrelation and non-validity of the multicollinearity assumption through Monte-Carlo studies. Results show that the Maximum Likelihood (ML) and the Hildreth and LU (HILU) estimators are generally preferable in estimating all the parameters of the model at all the levels of autocorrelation and multicollinearity. Consequently, when the these two forms of correlations can not be ascertained in a data set, it is more preferable to use either the ML or HILU estimator to estimate all parameters of the model.