Aim at the difficulty and low recognition rate of signature verification, this paper introduces biomimetic pattern recognition theory and applies it to the problem. According to the features of the signature samples, the coverage in the high-dimension feature space is built, one class of samples are all covered with a super-sausage neuron chain. As the radius selection of the super-sausage neurons maybe unreasonable, unwanted area may be covered and correct recognition rate will reduce. So this paper uses the relationship of the distance between the two training samples and the average distance of all the neurons to adjust the radius of the super-sausage neuron automatically. Finally, the experiments show that compared to traditional pattern recognition method, biomimetic pattern recognition theory used in signature verification have a better recognition result and is more effective.