A multisensor system combined with multivariate analysis was applied to the characterization of red wines and to the quantification of the grape variety percentage. The proposed system, known as hybrid electronic tongue, consists of a colorimetric optofluidic system and an array of electrochemical sensors. Three monovarietal red wines were studied: Pinot Noir, Merlot and Cabernet Sauvignon. Homemade mixtures were elaborated from these wines according to a Simplex experimental design with 60 samples. The data obtained were treated using advanced chemometric tools like Principal Component Analysis (PCA) and Soft Independent Modeling Class Analogy (SIMCA) for the classification of the wine mixtures and Partial Least Squares (PLS) regression for the quantification of the grape variety composition. The results have shown a good classification of the grape varieties and the identification of the mixtures with Pinot Noir up to 75%. Besides, using the PLS regression, the system has demonstrated a high potential for quantifying the percentage of each grape variety.