Authors: Sebastiano Battiato, Alessandro Ortis, Francesca Trenta, Lorenzo Ascari, Mara Politi, Consolata Siniscalco Description: With the spread of technology in several fields, there is an increasing demand to automate specialized tasks that usually require human involvement in order to maximize efficiency and reduce processing time. Pollen identification and classification is a proper example to be treated in the Palynology field, which has been an expensive qualitative process, involving observation and discrimination of features by highly qualified experts. Although it is the most accurate and useful method, it is a time-consuming process that slowed down the research progress. In this paper, we present a dataset composed of more than 13.000 objects, identified by an appropriate segmentation pipeline applied on aerobiological samples. Besides, we present the results obtained from the classification of these objects by taking advantage of several Machine Learning techniques, discussing which approaches have produced the most satisfactory results, and outlining the challenges we had to face to accomplish the task.