SPM is a powerful technique for the comparison of functional imaging data sets among groups of patients. While this technique has been widely applied in studies of adults, it has rarely been applied to studies of children, due in part to the lack of validation of the spatial normalization procedure in children of different ages. In order to determine if spatial normalization of FDG PET images using SPM96 to an adult template can be successfully applied in children, we applied PET-derived transformation parameters to coregistered MRI images. We then compared contours of spatially normalized MRI images obtained from 13 children with epilepsy (ages 2–14 years, mean 7.6 ± 3.9 years) with those derived from 17 adult controls (mean age 27.6 ± 4.5 years). Contours of spatially normalized MRI image volumes derived from the pediatric group were more variable than those obtained from adult controls. The average deviation from the mean adult contour was age-dependent and decreased with age (average deviation (mm) = 2.22 (mm) − 0.021 (mm/year) × years, r = 0.70, P < 0.001). Separate SPM analyses were performed for children less than 6 years (N₁ = 6) and for children between 6 and 14 years of age (N₂ = 7). SPM analyses performed in both pediatric groups showed significant regions of hypometabolism in locations consistent with their epileptic foci. SPM analyses in the younger group also showed significant artifacts. Therefore, the error associated with spatial normalization of pediatric brains to an adult template in children less than 6 years of age precludes the application of statistical parametric mapping in this age group. Although the error in the spatial normalization procedure for children ages 6 to 14 years is higher than in adults, it appears that this error does not result in artifacts in the SPM analysis. Furthermore, in contrast our previous studies showing large age-related changes in the absolute glucose metabolic rate at puberty, the SPM analysis showed children over 6 years of age appear to display the same pattern of glucose utilization as adults. However, small differences in the pattern of glucose utilization which might occur during late childhood and adolescence may not have been detected due to the sample size.