A 1-year time series of fungal spore concentrations has been used to calibrate an artificial neural network for the estimation of Alternaria and Pleospora concentrations associated to observed meteorological variables. Analysis of the results revealed that the daily average values of these meteorological variables are suitable to predict with high confidence the number of fungal spores that are actually observed. The calibrated neural network has also been used randomizing each single input parameter in order to evaluate which meteorological variable contributes more to the formation and the depletion of the selected fungal spores. Emphasis is given to the possibility of using the proposed model for operational activities, predicting the future spore concentrations on the basis of meteorological forecasts.