The automated recognition of fossil planktonic foraminifera in thin section (TS) samples can bring benefit through reduction of time, costs, and human effort for biostratigraphic analyses. In this work we present the outcomes of an Eni internal Research and Development project aimed at evaluating the feasibility of automated recognition of microfossils by means of computer vision. We trained two Deep-Learning (DL) models to first identify fossil candidates within TS samples and subsequently classify selected taxa among 66 registered Oligocene-Miocene planktonic foraminifera species. In the step of fossil detection, we achieved a mean average precision of 81.4% in extracting bounding boxes containing fossils from raw TS photomicrographs. In the subsequent fossil classification step, we obtained a top-1 accuracy of 70% (i.e., in approximately 70% of cases, the label assigned by the biostratigraphers coincided with the model best guess) and a top-3 accuracy of 88% (i.e., in 88% of cases, it was included within the top-3 species predicted by the model). Given these promising performances, the fossil detection and classification pipeline has been deployed in a web application that indicates fossil-containing bounding boxes and suggests the top-3 most likely species for each sample. Biostratigraphers’ corrections/confirmations are stored to be used for future re-trainings and incrementally improve the performances of the algorithms.