In this study, an artificial neural network (ANN) approach was applied for modeling the relation between environmental variables and daily change in the Cleanness Index (Delta CI, a measure of performance loss due to soiling) of photovoltaic modules in the field in Doha, Qatar. The daily Delta CI was examined among a number of three-dimensional intervals of the daily mean of the environmental variables (i.e., the intervals of two environmental variables were presented on x and y dimensions, and average values of daily Delta CI on the third dimension), in order to qualitatively establish the relations that might help to develop improved PV soiling prediction models. Then, an ANN-based model was set up to simulate the relationship between daily Delta CI and environmental variables and compared with a linear regression model, both models using the same input variables, including present day and previous day environmental conditions, and cumulative exposure time. Strong interactions were observed among environmental variables PM10, relative humidity (RH) and wind speed (WS) regarding their effect on the daily Delta CI. Overall, higher PM10 resulted in more negative daily Delta CI (Le. the module became more soiled), and this effect was more visible at low WS and RH levels, but at high WS ( > 4 m s(-1)) and high RH ( > 65%) levels, PM10, had no significant (p > 0.05, two tailed t-test) effect on daily Delta CI. Mostly, WS and RH determined how much airborne dust accumulates on the module surfaces and thereby affects the output of the PV modules. Higher WS typically favored more positive daily Delta CI when RH was low, but at higher RH levels ( > 50%) daily Delta CI was more likely to be negative with increasing WS. In fact, high RH levels were related to negative daily Delta CI only at higher WS levels ( > 2 m s(-1)); at lower WS levels RH had no significant effect on daily Delta CI. These effects were apparently due to the deposition-resuspension mechanisms of dust accumulation on the PV panel surfaces. The ANN model performed significantly better in predicting daily Delta CI as well as cumulative CI than the linear model in term of R-2 values and statistical error indexes. The previous day environmental conditions had a significant effect on the modeling outcome. The inclusion of the wind gustiness and cumulative exposure time also considerably improved the model prediction capability. The advantage of the ANN-based model is its simplicity, ease of data fitting and no requirement of an accurate mathematical model.