The problem of durability of fuel cell technology is central for its spreading and commercialization. There is therefore a growing need to build accurate diagnosis tools which can give the operating state of the fuel cell during their use. When supervised machine learning approaches are used to build such diagnosis tools, they generally require a large amount of labeled data. Collection and annotation of data can be either difficult to perform or time consuming. In this paper, authors are interested in the monitoring of fuel cells in an unsupervised framework, meaning that no labels are required to learn the diagnosis model. The aim is to build a monitoring tool able to easily visualize the State Of Health of full cells from electrochemical impedance spectroscopy measures, showing thus its evolution from fault free case ("normal" behaviour) to defective classes such as drying or flooding. The proposed approach is based on Self Organizing Maps (SOM) which have shown their performance to solve fault detection and prediction in many industrial systems. By automatically visualizing the data into a two-dimensional space, the interpretation of the results have become easy and instinctive. The approach also allows the clustering of the data into different groups of classes, thus enabling the classification of new observations. Experimental results carried out on real data sets have shown the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approaches.