The CCT diagrams of a class of Fe-(0.1-0.6)C-(0.4-2.0)Si-(0.4-2.0) Mn-(0.5-2.0)Cr-(0.0-0.8)Mo steels are predicted by an artificial neural network (ANN) model. The results indicate that an increase in carbon concentration (C wt%) gives rise to a decrease in bainite start (BS) temperature. The rate of decrease depends also on cooling rate. Additions of Si, Mn, Cr and Mo all decrease the bainite start temperature. The dependencies for different alloying elements vary: 32, 100-120, 100-130, and 70-150 degrees C per wt% of Si, Mn, Cr and Mo, respectively. Mn shifts the whole bainite transformation region to the far right-hand side of the CCT diagram, while C, Cr, and Mo have considerable, and Si has minor effects on the incubation period of bainite. Mn and Cr significantly decrease the MS temperature, while Si only has a minor influence. When Mo < 0.5 wt% it has a minor influence, whilst when Mo > 0.5 wt%, it increases MS temperature. Quasi-isochronal and quasi-isothermal methods have been used to analyze the influence of the proportion of Mo to C upon the BS and incubation period. Attempts, for qualitative explanations using the shear and diffusion mechanism, as well as a certain amount of thermodynamic analysis, have been made to interpret the influence of alloying elements on the nucleation of the bainite reaction. The results support that bainite reaction takes place utilizing a diffusion-controlled mechanism.