This article deals with the Bayesian approach for estimating the parameters of nonlinear growth functions used for modelling the body weight of farm animals. Gompertz, Brody, Von Bertalanffy and Logistic growth models were employed on the basis of nonlinear least squares and Bayesian methods. Using the monthly body weight data of 2070 weight records of Thalli sheep from birth to 24 months of age for both sexes (male and female) and type of births(single and twin), the estimates of parameters and their credible intervals were obtained from posterior distributions. The overall goodness of fit of classical method was calculated; namely, Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2) and root mean square error (RMSE). Deviance information criterion (DIC) and the square of correlation (R2) between observed body weight and the predicted value’s marginal density means and RMSE were used as evaluation measures in the Bayesian setup. Both approaches were strongly in favor of the Brody model as the best fit model than the competing nonlinear growth models for Thalli sheep. Results displayed that complex nonlinear functions can be easily fitted to weight-age data of animal via Bayesian approach.