Preliminary research indicates that a visual recording of one’s activities may be beneficial for sufferers of neurodegenerative diseases. However there exists a number of challenges in managing the vast quantities of data generated by lifelogging devices such as the SenseCam. Our work concentrates on the following areas within visual lifelogging: Segmenting sequences of images into events (e.g. breakfast, at meeting); retrieving similar events (“what other times was I at the park?”); determining most important events (meeting an old friend is more important than breakfast); selection of ideal keyframe to provide an event summary; and augmenting lifeLog events with images taken by millions of users from ‘Web 2.0’ websites (“show me other pictures of the Statue of Liberty to augment my own lifelog images”).