[en] A critical heat flux (CHF) prediction method using an artificial neural network(ANN) was evaluated for application to the high-heat-flux (HHF) subcooled flow boiling. The developed ANN predictions were compared with the experimental database consisting of a total of 3069 CHF data points. Also, the prediction performance by the ANN was compared with those by mechanistic models and a look up table technique. The parameter ranges of the experimental data are: 0.33 ? D ? 37.5 mm, 0.002 ? L ? 4 m, 0.37 ? G ?134 Mg/m² s, 0.1 ? P ? 20 MPa, 50 ? Δh_sub, _in ? 1660 kJ/kg, and 1.1 ? q_CHF ? 276 MW/m². It was found that 91.5% of the total data points were predicted within a ± 20% error band, which showed the best prediction performance among the existing CHF prediction methods considered. The ANN method is likely to be suitable for the HHF subcooled flow boiling CHF