An artificial neural network (ANN) model for predicting transformed microstructure in conventional rolling process and thermomechanical controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted results show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, microstructure and properties in TMCP. An artificial neural network (ANN) model for predicting transformed microstructure in conventional rolling process and thermomechanical controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted results show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, microstructure and properties in TMCP.