The effects of cyanidation conditions on gold dissolution were studied by artificial neural network (ANN) modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P80 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients (R2) of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.998 1, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively. It is verified that the predicted values of ANN coincide well with the experimental results. The effects of cyanidation conditions on gold dissolution were studied by artificial neural network (ANN) modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P80 of particle, NaCN content in cyanide media, A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients (R2) of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.998 1, respectively Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively. It is verified that the predicted values ​​of ANN coincide well with the experiment al results.