The role and construction of Stein-rule estimators in multivariate unstructural model is discussed when some prior information about the regression coefficients is available in the form of exact linear restrictions. The additional information in the forms of covariance matrix of measurement errors and reliability matrix of explanatory variables is used for the construction of consistent estimators. Two families of Stein-rule estimators are proposed using each type of additional information which are consistent as well as satisfy the exact linear restrictions. The distribution of measurement errors is assumed to be not necessarily normally distributed. The asymptotic distribution of the proposed families of Stein-rule estimators are derived and studied. The finite sample properties of the estimators are studied through a Monte-Carlo simulation experiment.