Gasoline blending is of pertinent importance in refinery operations owing to the fact that gasoline gives about 60 -70 % of the refinery profit. The blending process is essential to obtain gasoline in the demanded quantities and ensure property specifications are met. Two model equations, multivariable nonlinear and multivariable exponential are proposed in this study which are useful in predicting three significant properties of a motor gasoline: research octane number, reid vapour pressure and specific gravity. Gasoline blend data obtained from four different streams: straight run gasoline, straight run naphtha, reformate and fluidized catalytically cracked gasoline have been subjected to multivariate regression analysis with the aid of a statistical software to ascertain the fitness of the proposed equations in predicting the research octane number, reid vapor pressure and the specific gravity of the resulting premium motor spirit. The results of the regression analysis showed that the nonlinear multivariable models proposed gave a good fit as evidenced by the value of the coefficient of determination R 2 = 0.988 & 0.994 for the research octane number, 0.853 & 0.883 for the reid vapor pressure and 0.988 for specific gravity. In conclusion, the proposed model equations were fit to the data, found to be adequate, and therefore could be used for prediction of the blend gasoline properties.