In the age of Information Disorder, Satire is one of its phenomena mainly occurring in the context of social media. Satire represents an interesting study subject given that it can be easily confused with further forms of the disorder. The present work proposes and evaluates a set of linguistic features to build classifiers able to distinguish satires from other textual contents. The adopted features are firstly identified within the scientific literature and, secondly, ranked and filtered by means of the Information Gain index. Several experimentation activities show good performance for the aforementioned classifiers and an acceptable ability to generalize for the models trained with such features.