Chemical vapor analysis devices are booming, thanks to a growing need in areas such as security and quality control. These control devices are based on various technologies that are the subject of important researches in an ever-growing community of physicists and electronics. However, the data from these sensors are often processed by conventional algorithms poorly configured for the purpose of automatically recognizing target chemical compounds. These algorithms are often based on statistical models that are not always adapted to a limited number of learning data and demonstrated reproducibility problems for these kind of sensors. In this article, we propose to train fuzzy models and compare their performances with the classical methods of the state of the art, to show how practical they can be for such applications. Three different uses cases will be studied: toxic chemicals recognition, detection of counterfeit coffee capsules and detection of a chemical weapon among everyday products.