•Peri-fissural nodules (PFNs) have been proven to be bening nodules, for which no follow-up is needed.•Automatic classsification of PFNs would make lung cancer screening more efficient and reduce the number of follow-up.•Automatic classsification of PFNs would make lung cancer screening more efficient and reduce the number of follow-up.•State-of-the-art machine learning techniques approach human performance in PFNs classification. Display omitted In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.