A three-layer feed forward Artificial Neural Network was trained to predict plasma hormones and liver enzymes in broiler chickens. Six diet parameters are selected as inputs (predictor) and eleven performances, plasma hormones or liver enzymes are chosen as output (predicted) parameters of the model. Birds were fed diets containing different concentration of energy and protein. A data set with 100 individuals is divided in two subsets (each contains 50 individuals) for training and evaluation of the neural network. A comparison is made between laboratory analysis results and the neural network predicted values for plasma hormones and liver enzymes. The neural network accurately forecasted the considered values in the first (training) subset and invalid answers were not identified in the second verification subset. The proposed model were found to most effective identify performance, plasma hormones or liver enzymes for broiler chickens fed the diets on the known range of the energy and protein concentration. Sensitivity analysis shows the dependency evaluation of the output parameters with respect to the inputs.