Abstract: An approach of grading fertile eggs based on computer vision was presented. The weight and contour regularity were used as identification parameters of the shape of fertile eggs. According to computer vision measurement principle, the first artificial neural network(ANN) was used to detect dominant points of fertile eggs, such as size of fertile eggs obtained, then m-bands wavelet transform was used for features extraction of origin edge data. The second ANN identif regularity of shape. Results from experiments confirmed that the consistency identified by computer vision grading can reach over 93% in comparison with manual grading.