Chemometric tools were applied for exploratory analysis and classification of fuel blends using the combined information on Fourier transform infrared spectroscopy and stable isotope analysis through isotope ratio mass spectrometry. Principal component analysisand hierarchical clustering analysis were applied for exploratory analysis, while support vector machine (SVM) was used to classify the biodiesel/diesel blends. All of the chemometric models used present better results from the combination of spectral information with isotopic data for biodiesel contents of over 10% in the mixture, with the best results being Obtained from the SVM classification. Therefore, the development presented in this paper could become an important technique to improve the discrimination of the feedstock used in biodiesel production and a resource for quality control in industry.