This project was undertaken with the aim of developing control techniques and data processing methods to allow a relatively simple electronic nose developed for robotics applications to classify a wide range of odours. The electronic nose used tin oxide gas sensors as the main sensing elements. By modulating the sensor temperature inside the sensors%2C additional information was gathered which helps to identify unknown odours. A simple K-nearest neighbor algorithm was implemented as the basis of a pattern recognition system for recognizing odours presented to the system. Further%2C we considered ways of compressing the stored data and techniques for finding the best match among the trained data using Principal Component Analysis.