The relative abilities of the multilayer perceptron, radial basis function, asymmetric radial basis function and learning vector quantization artificial neural networks (ANNs) and two non-neural methods to identify fungal spores were compared. ANNs were trained on morphometric data from spores of Pestalotiopsis spp. and a few species in the related Truncatella and Monochaetia. The optimized neural and statistical classifiers had similar identification success on an unseen data set – between 76·0 and 78·8% of a 16-species group and between 63·0 and 67·7% of a 19-species group. The relative merits of each classifier are discussed, as is the potential of ANNs in mycology.