Positron emission tomography (PET) and the ligand [(18)F]fluoromisonidazole ((18)F-FMISO) have been used to image hypoxic tissue in the brain following acute stroke. Existing region of interest (ROI)-based methods of analysis are time consuming and operator-dependent. We describe and validate a method of statistical parametric mapping to identify regions of increased (18)F-FMISO uptake. The (18)F-FMISO PET images were transformed into a standardized coordinate space and intensity normalized. Then t statistic maps were created using a pooled estimate of variance. Statistical inference was based on the theory of Gaussian Random Fields. We examined the homogeneity of variance in normal subjects and the influence of normalization by mean whole brain activity versus mean activity in the contralateral hemisphere. Validity of the distributional assumptions inherent in parametric analysis was tested by comparison with a non-parametric method. The results of parametric analysis were also compared with those obtained with the existing ROI-based method. Variance in uptake at each voxel in normal subjects was homogeneous and not affected by mean voxel activity or distance from the centre of the image. The method of normalization influenced results significantly. Normalization by whole brain mean activity resulted in a smaller volume of tissue being classified as hypoxic compared to normalisation by mean activity in the contralateral hemisphere. The ROI-based method was subject to interobserver variability with a coefficient of variability of 16%. The volumes of hypoxic tissue identified by parametric and nonparametric methods were highly correlated (r = 0.99). These findings suggest that using a pooled variance and contralateral hemisphere normalisation, statistical parametric mapping can be used to objectively identify regions of increased (18)F-FMISO uptake following acute stroke in individual subjects.