Non-functional Requirements (NFRs) such as software quality attributes, software design constraints and software interface requirements hold crucial information about the constraints on the software system under development and its behavior. NFRs are subjective in nature and have a broad impact on the system as a whole. Being distinct from Functional Requirements (FR), NFRs are dealt with special attention, as they play an integral role during software modeling and development. However, since Software Requirements Specification (SRS) documents, in practice, are written in natural language, solely holding the perspectives of the clients, the documents often end up with FR and NFR statements mixed together in the same paragraphs. It is, therefore, left upon the software analysts to classify and separate them manually. The research, presented in this talk, aims to automate the process of detecting NFR sentences by using a text classifier equipped with a part-of-speech (POS) tagger. The results achieved an accuracy of 98.56% using 10-folds-cross-validation over a dataset used in the literature. The research, reported here, is part of a larger project aimed at applying Natural Language Processing techniques in Software Requirements Engineering.