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Although the great interest of development of performed gas and liquid sensors, lack of cross-sensitivity still remains the major drawback of electronic sensing systems such as electronic nose and tongue. We propose here an approach aimed at overcoming this shortcoming. So a performed data fusion method of electronic nose and tongue was used in order to classify five Virgin Olive Oils (VOOs) picked up from five Moroccan geographical areas. The electronic nose instrument consists of five commercial available MOS TGS gas sensors and the electronic tongue was designed using four voltammetric electrodes. Two techniques, i.e., Principal Component Analysis (PCA) and Support Vector Machines (SVMs) were used to develop classification models using as inputs specific features extracted from the collected sensor signals. Great enhancement in successful discrimination between all VOOs was achieved when compared to the individual systems due to a performed low-level of abstraction data fusion.