The present paper mainly deals with the prediction of blast-induced ground vibration level at a Magnesite Mine in tecto-dynamically vulnerable hilly terrain in Himalayan region in India. The ground vibration was monitored to calculate the safe charge of explosive to avoid the continuous complaints from the nearby villagers. The safe charge of explosive and peak particle velocity (PPV) were recorded for 75 blast events (150 blast data sets) at various distances. These data sets were used and analysed by the widely used vibration predictors. From the four predictors, vibration levels were calculated and compared with new monitored 20 blast data sets. Again, the same data sets were used to validate and test the three-layer feed-forward back-propagation neural network to predict the PPV values. The same 20 data sets were used to compare the results by the artificial neural network (ANN). Among all the predictors, a very poor correlation was found, whereas ANN provides very near prediction with high degree of correlation.