The great interest for the development of performed gas sensors has now an increasing application field -- the electronic nose. We propose here to develop such a system for an environmental application in the field of the air quality control. Our application is based on the study of the learning process of our sensor system for a performed discrimination of a refrigerant gas forane 134a and carbon dioxide in a humidity and temperature controlled atmosphere. In this paper, we present first the characterisation results obtained for the metal oxide sensors used in this application. Afterwards, we propose to compare the discrimination results of the gases with several representative variables by using two pattern recognition methods: principal component analysis (PCA) and discriminant factorial analysis (DFA). We show the importance to use two types of complementary variables: the steady-state conductance and the conductance dynamic slope. We also propose a new method to reduce the drift effect and then we show the discrimination of the gases and also the identification of unknown cases by the way of the created decisive law.