The development of a neurocomputing technique to forecast the average winter shower in India has been modeled from 48 years of records (1950-1998). The complexities in the rainfall-sea surface temperature relationships have been statistically analyzed along with the collinearity diagnostics. The presence of multicollinearity has been revealed and a variable selection has been executed accordingly. The absence of persistence has also been revealed. For this reason, an Artificial Neural Net Model as a predictive tool for the said meteorological event in the form of a Multiple Layer Perceptron has been generated with a sea surface temperature anomaly and monthly average winter shower data over India during the above period. After proper training and testing, a Neural Net model with small prediction error is developed and the supremacy of the Artificial Neural Net over conventional statistical predictive procedures has been established statistically. © IWA Publishing 2012.