One of the limiting factors for the spreading of the fuel cell technology is the durability and researches to extend their lifetime are being done world-widely. We present here a pattern recognition approach aiming to estimate fuel cell operating time based on electrochemical impedance spectroscopy measurements. It consists in first extracting features from the impedance spectrum. For that purpose, two approaches have been investigated. In the first one, particular points of the spectrum are empirically extracted as features. In the second approach, a parametric modelling is performed to extract features from both the real and the imaginary parts of the impedance spectrum. In particular, a latent regression model is used to automatically split the spectrum into several segments that are approximated by polynomials. The number of segments is adjusted taking account the a priori knowledge about the physical behaviour of fuel cell components. Then, a linear regression model using different subsets of extracted features is employed for the estimation of fuel cell operating time. The performances of the proposed approach are evaluated on experimental data set to show its feasibility. Being able to estimate the fuel cell operating time, and consequently its remaining duration life, these results could lead to interesting perspectives for predictive maintenance policy of fuel cells.