During the iron printing process, the defect detection system has a problem that the accuracy of defect detection is not high, and intelligent judgment cannot be achieved for the overall product quality. This paper aims to propose a classification algorithm suitable for on-line detection of printed iron based on GRNN-PNN neural network. Wavelet transform was performed on printed iron sheet to extract low-frequency information. The defects in low-frequency information were located, and the defective areas were marked and segmented. A GRNN neural network was constructed based on defect area, perimeter and other evaluation indexes to classify defects. A PNN neural network was constructed to intelligently determine whether the overall product was a qualified product. The average time of GRNN-PNN was 0.69s, which met the factory's requirement on the response time of online defect detection. The accuracy of GRNN-PNN multi-classification was 86%, which can classify the main defects generated during the iron printing process. The sensitivity of the two classifications was 96%, which can accurately judge the overall qualification of the product. Under 5% salt and pepper noise, the accuracy rate was 63%, and GRNN-PNN had good robustness. The design can accurately classify and intelligently judge the defects of printed iron. The GRNN-PNN neural network can further improve the detection accuracy during the iron printing process. The GRNN-PNN neural network can help quality inspectors to judge the production quality in time.