Identification of butterfly species is essential because they are directly associated with crop plants used for human and animal consumption. However, the widely used reliable methods for butterfly identification are not efficient due to complicated butterfly shapes. We previously developed a novel shape recognition method that uses branch length similarity (BLS) entropy, which is a simple branching network consisting of a single node and branches. The method has been successfully applied to recognize battle tanks and characterize human faces with different emotions. In the present study, we used the BLS entropy profile (an assemble of BLS entropies) as an input feature in a feed-forward back-propagation artificial neural network to identify butterfly species according to their shapes when viewed from different angles (for vertically adjustable angle, ${ heta}={pm}10^{circ}$, ${pm}20^{circ}$, ${cdots}$, ${pm}60^{circ}$ and for horizontally adjustable angle, ${varphi}={pm}10^{circ}$, ${pm}20^{circ}$, ${cdots}$, ${pm}60^{circ}$). In the field, butterfly images are generally captured obliquely by camera due to butterfly alignment and viewer positioning, which generates various shapes for a given specimen. To generate different shapes of a butterfly when viewed from different angles, we projected the shapes captured from top-view to a plane rotated through angles ${ heta}$ and ${varphi}$. Projected shapes with differing ${ heta}$ and ${varphi}$ values were used as training data for the neural network and other shapes were used as test data. Experimental results showed that our method successfully identified various butterfly shapes. In addition, we briefly discuss extension of the method to identify more complicated images of different butterfly species.