The need for reliable detection of head movement artefacts in an ambulatory EEG system has been demonstrated in previous work. In this paper we propose the use of gyroscopes in detecting artefacts in EEG. A collection of features are extracted from the gyroscope signals and ranked using Mutual Information Evaluation Function. Linear Discriminant Analysis is subsequently used as a means of seperating between normal EEG and artefacts. A Support Vector Machine classifier is also applied to the gyroscope feature signals. Results indicate good separation between gyroscope features extracted from normal EEG and those extracted from artefacts arising from head movement, providing a strong argument for including gyroscope signals as features in the classification of head movement artefacts.