Polarisation curves performed at the Fuel Cell System Laboratory (FC LAB) at Belfort on a PEM fuel cell stack using a homemade fully instrumented test bench led to more than 100 variables depending on time. Visualising and analysing all the different test variables are complex. In this work, we show how the Principal Component Analysis (PCA) method helps to explore correlations between variables and similarities between measurements at a specific sampling time (individuals). To complete this method, an empirical model of the PEM fuel cell is proposed by linking the different input parameters to the cell voltage using Multiple Linear Regressions. Proton exchange membrane (PEM) fuel cell; Principal Component Analysis (PCA); Multiple Linear Regression; statistical analysis.