Raman spectroscopy is largely informative about organic matter (OM) chemical composition and structure and can therefore be applied to evaluate the thermal maturity of coals, carbonaceous materials and kerogens, i.e., their degree of evolution during burial heating. The evaluation of OM maturity is commonly performed by band-fitting followed by the measure of suitable spectral parameters (typically band separations and area ratios). However, this procedure can introduce some subjectivity both in the number and line-shape of the fitting bands and in the definition and selection of the most meaningful spectroscopic parameters. Here, a principal component analysis-partial least squares regression (PCA-PLS) chemometric approach for the treatment of spectra is presented, that is intrinsically unaffected by such arbitrariness. In fact, the total spectrum is analyzed in order to extract the spectroscopic ranges of maximum variance in a multivariate approach. In addition, being automated, the treatment is well-suited to huge sets, as those commonly collected to appropriately and reliably evaluate the chemical-physical and geological variables in an exploration well. As a case study the maturity profile (maximum paleotemperature) of OM from a well drilled in the Lower Congo basin (offshore Angola) is evaluated, both by spectral parameters and by PCA-PLS analysis, with results in good agreement on the basis of the prediction error. A set of coals is adopted as a reference to predict the reflectance in oil (%R-o) profile from the Raman data. The comparison of the calculated %R, values with the experimental ones, presenting, in this case, some suppressed values, shows that the Raman analysis is not affected by underestimation of maturity as the vitrinite reflectance may be.