Variety purity is the most important quality index of corn hybrid seed, corn variety purity has a great influence on corn production and quality, but variety identification is very difficult. Electrophoretic method wasa new variety identification method developed in recent years. It was rapid, simple, accurate and reproducible. Because the electrophoretogram recognition relied on human completely, this demanded users having high technique level. In this paper, a computer vision system was developed for corn hybrid variety identification by corn seed electrophoretogram processing and analysis. The method of qasi-wavelet edge multi-scale detection based on Gaussian filter and vector differential operator was used to extract feature of characteristic bands successfully. A BP neural network classifier was designed for classification of corn hybrid electrophoretogram, the test results demonstrated that classification accuracy is 96%. Based on the proposed theories and methods, a set of applied software was developed for corn hybrid variety identification. It was tested with 30 kinds of corn hybrid, the results demonstrated that the system provided an accuracy of 95% compared to experts inspection.