Joint transform correlator based on opto-electronic hybrid processing is discussed to recognize high similarity industrial parts images. Experiment system of joint transform correlator is presented. Muitilevel simulating targets based on basic shapes are constructed in order to simulate different industrial images. Joint image similarity degree for joint transform correlator is defined based on morphology method. Joint images are graded based on image similarity degree. Two kinds of method based on principle improment and non-principle improment are refined for high similarity image recognition in order to reduce false decision. Octagon with holes

octagon and pentagon are selected as input images from basic shapes and pentagon is selected as reference image. Structure light pattern is put forward to encode joint images for non-principle improment. Complementary encoding method based on morphological hit-or-miss transform is applied to code joint images for principle improment. Distinct effect is acquired with industrial basic shapes recognition by computer simulation. The results indicate that we can recognize high similarity images by raising JTC recognize ability or reducing image similarity degree.