We present the Pico-1 electronic nose based on thin-film semiconductor sensors and an application to the analysis of two groups of seven coffees each. Cups of coffee were also analyzed by two panels of trained judges who assessed quantitative descriptors and a global index (called Hedonic Index, HI) characterizing the sensorial appeal of the coffee. Two tasks are performed by Pico-1. First, for each group, we performed the classification of the seven different coffee types using principal component analysis and multilayer perceptrons for the data analysis. Classification rates were above 90%. Secondly, the panel test descriptors were predicted starting from the measurements performed with Pico-1. The standard deviations for the prediction of the HI are comparable to the uncertainty of the HI itself (0.2 on a 1 to 9 scale for one group of coffees).