This paper addresses the automatic identification of often traversedroutes for assisted living applications using WLAN technology in addition to other modalities. This problem is complicated by a number of factors, including the changing and noisy nature of the WLAN channel, the need to track users seamlessly in both indoor and outdoor environments, the need for robustness to slight deviations in the precise path taken, and speed, along a route. In this work commonly traversed routes are identified by clustering based on sensed data, two of which take the form of wireless signals: GPS and WLAN. The latter is particularly important as it can be used both indoors and outdoors. In addition an efficient image matching algorithm is implemented to process data from images automatically taken along the route. In this work a finite number of routes were identified within the DCU campus. Each route was traversed many times over a period of 6 weeks and data sequences collected automatically on each occasion. Each such traversal of a route is referred to as a trip in what follows. Section (2) outlines the use of Multidimensional Time Warping in order to automatically cluster trips corresponding to specific routes based on wireless and image data sensed on each trip. Section (3) outlines the manner in which data was sensed and presents clustering results for each modality individually as well as results based on a fusion of the data.