Switching between windows on a computer is a frequent activity, but finding and switching to the target window can be inefficient. This thesis aims to better un-derstand and support window switching. It explores two issues: (1) the lack of knowledge of how people currently interact with and switch between windows and (2) how window switching can be supported better. Having a good understanding of how users interact with windows is important for informing the design of new and improved window management tools. How-ever, there have been relatively few empirical studies of window manipulation on commonly used operating systems, and those that do exist may no longer reflect current use. To address this lack of knowledge a three week log-based longitudi-nal study of window use by 25 participants was conducted using the custom-made tool PyLogger, which recorded actual window switching behaviour. However, the analysis of longitudinal log data, such as the data gathered by PyLogger, is problematic as it is difficult to extract meaningful characterisations. Therefore, this thesis also presents a visualisation tool called Window Watcher that assists understanding and interpreting the low level event logs of window use generated by PyLogger. Window Watcher’s design objectives are described, and examples demonstrate the ways that it summarises and elucidates window use. The results of the PyLogger study provide an empirical characterisation of in-teraction with windows, and results include the following: (1) the participants had fewer windows open and visible than in previous studies; (2) window switching is a frequent activity; (3) several findings related to specific window switching tools, including that acquiring a particular window by navigating through application-grouped items on the Taskbar is slow, and that Alt+Tab is seldom used for re-trieving anything other than the most recently used window; (4) an updated clas-sification of stereotypical window management styles pilers, maximisers, near maximisers, and splatterers); and (5) there are strong window and application re-visitation patterns. Finally, implications of the results of the log study for the design of window switching tools are discussed.