To assess the quality of commercial lubricant oils a spectroscopic method was used in combination with multivariate regression techniques like multivariate multiple regression (MLR), Principal Components Regression (PCR) or Support Vector Machines for Regression,(SVR). The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. The use of Genetic Algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.