A machine vision system for fault detection in PET bottles is presented. The bottle inspector is divided in three modules for image acquisition of bottle finish, bottle wall and bottle bottom. The captured images are corrected by adaptive gamma correction. An algorithm based in the frequency filtering of n images for defect detection of bottle wall and bottle finish is proposed. We obtain a correct rate classification of 85.5 % in bottle finish, 80.64 % in bottle wall and 95.0 % in bottle bottom.