Drilling and Blasting are still considered to be the most economical method for rock excavation either on surface or underground. The explosive energy, which breaks the rockmass, is not fully utilized for this purpose. Only 20–30% of explosive energy is utilized for fragmenting the rockmass and the rest wasted away in the form of ground vibration, air blast, noise, fly rock, back breaks, etc. Among them, ground vibration is considered to have the most damaging effect. A number of predictor equations have been proposed by various researchers to predict ground vibration prior to blasting. Still, it is difficult to recommend any one predictor for a particular ground condition because ground vibration is influenced by a number of parameters. These parameters are either controllable or non-controllable like blast geometry, explosive types, rock strength properties, joints patterns, etc. In the present paper, an attempt has been made to predict the ground vibration using an Artificial Neural Network incorporating large number of parameters, which affect the ground vibration. Results are also compared with the values obtained from regression analysis and observed field data sets. Finally, it is found that the neural network approach is more accurate and able to predict the value of blast vibration without increasing error with increasing number of inputs and non-linearity among these.