Location aware computing has become an important area in the field of telecommunications due to the large increase in the number of mobile communications devices. A need has arisen to calculate the position of these devices in all environments. Indoors, this challenge has yet to be overcome as the success of satellite positioning outdoors has not been repeated indoors. Using 802.11 Wi-Fi signals is an attractive and reasonably affordable option for dealing with this problem of widespread tracking in an indoor environment. However, current systems cannot provide continuous real time tracking of a moving target and lose accuracy when signal coverage is poor. This is due to the underlying characteristics of radio waves (i.e. multipath effects) and due to infrastructural requirements. It is these problems that HABITS (History Aware Based Indoor Tracking System) attempts to overcome with the creation of a system that probabilistically learns the movement patterns of a person and uses this knowledge to intelligently predict where the person will go. HABITS models human movement patterns by applying a discrete Bayesian filter to predict the areas that will, or will not, be visited in the future. This thesis discusses the HABITS model and implementation which aim to overcome weaknesses in existing Real Time Location Systems (RTLS) by using the human approach of making educated guesses about future location. The hypothesis of this thesis is that knowledge of people’s historical movement habits facilitates prediction by computational means, of their future locations in the short, medium and long term. The primary research question is whether the tracking capabilities of existing RTLS can be improved automatically by knowledge of previous movement and by the application of a combination of artificial intelligence approaches. The HABITS model is designed to be generic and to operate on top of any RTLS, however the implementation described in this thesis uses the 802.11 Wi-Fi Ekahau RTLS. Results show that HABITS improves on the standard Ekahau RTLS in term of accuracy (overcoming black spots), yield (giving position fixes when Ekahau cannot), cost (less APs are required than are recommended by Ekahau) and prediction (short term predictions are available from HABITS). These are features that no other indoor tracking system currently provides. Testing of HABITS shows that it gives comparable levels of accuracy to those achieved by doubling the number of access points. It is twice as accurate as existing systems in dealing with signal black spots and it can predict the final destination of a person within the test environment almost 80% of the time. Numerous potential applications of HABITS exist ranging from healthcare and security to logistics and building automation.