The use of a miniaturized potentiometric electronic tongue based on low-selective polymeric sensors was demonstrated for the discrimination and sensory characterization of coffee samples. The sensor array was able to discriminate 21 varieties of coffee with an average accuracy of 91.3% by combining hand-crafted features, predictor importance methods and trained classification models. Moreover, the e-tongue supported by a single regressor could be successfully trained to predict simultaneously the intensity of 13 coffee descriptors by leveraging dimensionality reduction to learn their interdependence. Sensory profiles of 33 samples were reconstructed with a 0.78 RV coefficient of agreement with sensory data using a rigorous leave-one-coffee-out validation. This study emphasizes the advantages of data-driven sensing approaches based on training by examples to help increase sample throughput when exploring new formulations and accelerate product design cycles.