As computers have become more and more advanced, with even the most basic computer capable of tasks almost unimaginable only a decade ago, researchers and developers are focusing on improving the way that computers interact with people in their everyday lives. A core goal, therefore, is to develop a computer system which can understand and react appropriately to natural human behavior. A key requirement for such a system is the ability to automatically, and in real time, recognises human facial expressions. In addition, this must be successfully achieved regardless of the inherent differences in human faces or variations in lighting and other external conditions. The focus of this research was to develop such a system by evaluating and then utilizing the most appropriate of the many image processing techniques currently available, and, where appropriate, developing new methodologies and algorithms. The first key step in the system is to recognise a human face with acceptable levels of misses and false positives. This research analysed and evaluated a number of different face detection techniques, before developing a novel algorithm which combined phase congruency and template matching techniques. This novel algorithm provides key advantages over existing techniques because it can detect faces rotated to any angle, and it works in real time. Existing techniques could only recognise faces which were rotated less than 10 degrees (in either direction) and most could not work in real time due to excessive computational power requirements. The next step for the system is to enhance and extract the facial features. To successfully achieve the stated goal, the enhancement and extraction of the facial features must reduce the number of facial dimensions to ensure the system can operate in real time, as well as providing sufficient clear and detailed features to allow the facial expressions to be accurately recognised. This part of the system was successfully completed by developing a novel algorithm based on the existing Contrast Limited Adaptive Histogram Equalization technique which quickly and accurately represents facial features, and developing another novel algorithm which reduces the number of feature dimensions by combining radon transformation and fast Fourier transformation techniques, ensuring real time operation is possible. The final step for the system is to use the information provided by the first two steps to accurately recognise facial expressions. This is achieved using an SVM trained using a database including both real and computer generated facial images with various facial expressions. The system developed during this research can be utilised in a number of ways, and, most significantly, has the potential to revolutionise future interactions between humans and computers by assisting these reactions to become natural and intuitive. In addition, individual components of the system also have significant potential, with, for example, the algorithms which allow the recognition of an object regardless of its rotation under consideration as part of a project aiming to achieve non-invasive detection of early stage cancer cells.