Fully automated principal components analysis (PCA) was applied to dynamic 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) positron emission tomographic (PET) images obtained in 15 patients with previously treated head and neck cancer. PCA with time-activity curves incorporated kinetic information about FDG uptake, which improved tissue characterization on FDG PET images. The combination of standardized uptake value and PCA image sets likely will improve the reliability of tumor detection in head and neck cancers.