Visual lifelogging is the process of automatically recording images and other sensor data. Microsoft’s SenseCam is lifelogging camera have mostly been used in medical applications. Experience shows that the SenseCam can be an effective memory aid device, as it helps users to improve recollecting an experience. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge to deconstruct a sizeable collection of images into meaningful events for users. In this paper random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to “distinct significant events” in the visual lifelogs. Finally, the cross-correlation matrix is cleaned by separating the noisy part from non-noisy part of cross-correlation matrix C. Overall, the RMT technique is shown useful to detect major events in SenseCam images.