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The present investigation was carried out on 216 records of Osmanabadi goat and 208 records of Deccani sheep. The relative efficiency of three different methods, viz. Shaffer's formula method, multiple linear regression and artificial neural network for prediction of live body weight were investigated. Individual animals were weighed on electronic weighing balance along with their body measurements like lengths, height and girths were measured. The explanatory variables were body lengths, body height and chest girths while dependant variable was body weight. A multilayer feed forward neural network with back propagation of error learning mechanism was developed using artificial neural network using bayesian regularization algorithms. It was observed that artificial neural network was best fitted with in goat and sheep, with the adjusted R2 of 0.93, explained by its linear relationship with the explanatory variables in goat. However, the prediction accuracy (R2 value) was observed as 94.21% with 2.35 kg error. While in sheep, the adjusted R2 was 0.82 and the prediction accuracy (R2 value) was observed as 85.29% with 3.48 kg error. The multiple linear regressions observed the adjusted R2 of 0.894, and the prediction accuracy (R2 value) as 90.03% with 4.73 kg error. The correlation coefficients for different body measurements using three different methods were ranged from 0.952 to almost one.