The availability of web search has revolutionised the way people discover information, yet as search services maintain larger and larger indexes they are in danger of becoming a victim of their own success. Many common searches can return a vast number of web pages, many of which will be irrelevant to the searcher, and of which only about ten or twenty of the top-ranked results will be browsed. The problem is that while pages returned by a search may be relevant to the keywords entered, the keywords generally give only a partial expression of the searcher's information need. Personalised web search takes keywords from the user as an expression of their information need, but also uses additional information about the user (such as their preferences, community, location or history) to assist in determining the relevance of pages. There are many approaches to providing personalised web search, each with the aim of returning the results most relevant to the user ranked highest. The features that distinguish the approaches are the kind of information about the user that is used, the level of interaction with the user (explicit or implicit collection of data), how the information is stored (client-side or server-side), the algorithm used to incorporate the information about the user into the search and how information is presented to the user (mobile devices present some unique challenges in this respect). Some of these personalisation methods stem from techniques previously used in traditional information retrieval, whilst others are unique to the web environment. This chapter describes the many techniques that have been applied to adapt the web search process to the individual user. We also present a novel system that we are developing, which uses a client-side user profile to provide a personalised ranking of results from multiple search portals. We conclude with a brief consideration of the future of personalised search and how it may affect the development of the web.