Functional MRI (fMRI) carries the potential for non-invasive measurements of brain activity. Typically, what are referred to as activation images are actually thresholded statistical parametric maps. These maps possess large inter-session variability. This is especially problematic when applying fMRI to pre-surgical planning because of a higher requirement for intra-subject precision. The purpose of this study was to investigate the impact of residual movement artefacts on intra-subject and inter-subject variability in the observed fMRI activation. Ten subjects were examined using three different word-generation tasks. Two of the subjects were examined 10 times on 10 different days using the same paradigms. We systematically investigated one approach of correcting for residual movement effects: the inclusion of regressors describing movement-related effects in the design matrix of a General Linear Model (GLM). The data were analysed with and without modeling the residual movement artefacts and the impact on inter-session variance was assessed using F-contrasts. Inclusion of motion parameters in the analysis significantly reduced both the intra-subject as well as the inter-subject-variance