Automatic identification is an efficient technology for the identification of butterfly species and pest control. As the major agriculture and forest pest, butterflies can be accurately classified based on the taxonomic characters. However, such identification can be only achieved by a few insect experts with years of experience. In the study, the shape and texture of butterflies were investigated for the automatic identification of butterfly species in the digital images: Histograms of multi-scale curvature (HoMSC) and gray-level co-occurrence matrix of image blocks (GLCMoIB) were used to describe the shape and texture of butterfly wings, respectively. To get an accurate identification result, a weight-based k-nearest neighbor classifier was designed. In addition, 750 images of 50 butterfly species were used for the identification test. The accuracy rate of this automatic identification method reached 98%. The result suggested that the HoMSC and GLCMoIB features can be efficient for the identification of butterfly species.