Indonesia has vast forests, even ranked as the third largest forest in the world. However, currently many forest areas have been deforested or the phenomenon of losing tree cover and forest areas. Forest rehabilitation programs develop by prioritizing plant or tree species that have fast growth or are called fast growing species. However, many people do not know about these fast growing species. Even though knowledge about the types of fast growing plant species is very important for the community to have so that the community can find out which plants can accelerate forest rehabilitation. Fast growing species of plants can actually be identified from the shape of the leaves. This study aims to build a classification model for fast growing species plant images based on leaf images by applying the Radial Basis Function (RBF) artificial neural network algorithm with morphological feature extraction. Morphological feature extraction is used to identify the shape of an object in order to obtain feature values based on predetermined parameters. These features then become input for the RBF artificial neural network to obtain learning patterns. The RBF network has three layers that are feedforward so that it can support solving classification or pattern recognition problems. Based on the results of accuracy testing, an accuracy value of 87.50% was obtained. This means that the Radial Basis Function (RBF) neural network is able to classify fast growing plant species based on leaf images.