In this study, it was found that limiting the number of explanatory distillation curves input variables in a multivariate regression method to the most significant ones (10%, 50%, 90%, and 100% of recovered condensate volumes) provides a reliable model that can be used as a quick and inexpensive method for determining the octane rating of commercial gasoline. Octane numbers were predicted using a combination of distillation curves and multivariate calibration techniques of partial least squares regression (PLSR) and principal component regression (PCR). The root mean square error of prediction (RMSEP) evaluation for calibration and prediction sets for PLSR technique varies between 0.545 and 0.902 for MON and RON, respectively, which is relatively acceptable when compared to earlier studies. Comparing PLSR and PCR results revealed that PCR has slightly lower RMSEP values for MON and RON, 0.527 and 0.899, respectively. The fact that measurements of studied key temperatures are a routine part of gasoline quality control makes the current technique a promising alternative to the suggested PLS distillation curves models in the literature, which require a larger number of explanatory variables. This method can also replace the costly and time-consuming spectral analysis now used to determine the octane rating of gasoline.