Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004--2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and logx+1 transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.