In computational approaches to the study of variation among folk song melodies from oral culture, both global and local features of melodies have been used. From a computational point of view, the representation of a melody as a vector of global feature values, each summarizing an aspect of the entire melody, is attractive. However, from an annotation study on perceived melodic similarity and human categorization in music it followed that local features of melodies are most important to classify and recognize melodies. We compare both approaches in a computational classification task. In both cases, the discriminative power of features is assessed. We use a feature evaluation criterion that is based on the performance of a nearest-neighbour classifier. As distance measure for vectors of global features, we use the Euclidian distance. For the sequences of local features, we use the score of the Needleman–Wunsch alignment algorithm. In each of our comparisons, the local features correspond to the global features. In all cases, it appears that the local approach outperforms the global approach in a classification task for melodies, which indicates that local features carry more information about the identity of melodies. Therefore, locality is a crucial factor in modelling melodic similarity among folk song melodies.