Roasting is a critical process in coffee production, as it enables the development of flavor and aroma. At the same time, roasting may lead to the formation of nondesirable compounds, such as polycyclic aromatic hydrocarbons (PAHs). In this study, Arabica green coffee beans from Cuba were roasted under controlled conditions to monitor PAH formation during the roasting process. Roasting was performed in a pilot-spouted bed roaster, with the inlet air temperature varying from 180 to 260 °C, for roasting conditions ranging from 5 to 20 min. Several PAHs were determined in both roasted coffee samples and green coffee samples. Different models were tested, with more or less assumptions on the chemical phenomena, with a view to predict the system global behavior. Two kinds of models were used and compared: kinetic models (based on Arrhenius law) and statistical models (neural networks). The numbers of parameters to adjust differed for the tested models, varying from three to nine for the kinetic models and from five to 13 for the neural networks. Interesting results are presented, with satisfactory correlations between experimental and predicted concentrations for some PAHs, such as pyrene, benz[a]anthracene, chrysene, and anthracene.