Recent developments in the field of natural language processing (NLP) have increased capabilities in the domain of humanmachine communication. Employees for repetitive and static processes like question answering or resolving simple problems are being replaced by the increasingly present chatbots and virtual assistants. Until recently, communication and machines were considered two separate research domains. However, new technologies like attention mechanism and transformers architecture are closing the gap between the two domains to the level that, besides giving the correct answer to a question, machines are aware of user’s sentiments, hidden intentions, cultural differences etc. This thesis elaborates on the first step of sustainable communication with a human being: intent recognition. The paper research on existing methods and technologies in the field of NLP for the end objective to recognize the user’s intent. For the purpose of showing functionalities of the methods, a set of models were developed with accuracy over 90% for classifying intents in the domain of university processes like course enrollments and thesis registration. As an additional contribution of this paper is the research on the influence of language for intent recognition in a multilingual environment.