As an effective texture description operator, local binary patterns (LBP) has the advantages of low computation complexity, low memory consumption and clear principle. Damper-Shafter evidence theory satisfies the conditions weaker than Bayesian probability theory and can directly express states of "uncertain" and "don't know". To exploit the advantages of above two concepts, a new texture recognition method is proposed. Firstly, the approach computes image pyramid and uses the distributions of multi-scale LBP to measure the similarity between two texture images. Secondly, the method combines the similarity measurement between the test texture and each training sample to combine the information given by each training sample. Finally, the recognition result is determined by the maximum evidence among different texture classes. Experimental results show that the proposed method achieves a correction rate of 96.43%, and 91.67%, for data set 1 and data set 2, respectively, outperforming the original LBP based texture recognition algorithm.