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For License Plate Recognition (LPR) system, license plate character recognition rate is seriously affected by image quality. To resolve this problem, a new algorithm is proposed, in which Intersecting Cortical Model (ICM) is applied into license plate character recognition. ICM was derived from several visual cortex models, which can be applied to image feature extraction efficiently. In the new algorithm ICM was utilized to extract three features from size-normalized binary input character image. Based on these features, weighted voting was performed and final estimation of input character was made. Experiment results show that compared with common algorithms based on BP network, the new ICM based algorithm has higher recognition rate and stronger robustness, and is more convenient and flexible.