Visual lifelogging using wearable cameras accumulates large amounts of image data. To make them useful they are typically structured into events corresponding to episodes which occur during the wearer’s day. These events can be represented as a visual storyboard, a collection of chronologically ordered images which summarise the day’s happenings. In previous work, little attention has been paid to how to select the representative keyframes for a lifelogged event, apart from the fact that the image should be of good quality in terms of absence of blurring, motion artifacts, etc. In this paper we look at image aesthetics as a characteristic of wearable camera images. We show how this can be used in combination with content analysis and temporal offsets, to offer new ways for automatically selecting wearable camera keyframes. In this paper we implement several variations of the keyframe selection method and illustrate how it works using a publicly-available lifelog dataset.