Abstract: By using artificial Neural Network (NN) and considering the effects of the solar and geomagnetic activities on the ionosphere in this paper we brought out a method to forecast the ionospheric critical frequency, f0F2, in China up to 5 hours ahead. The inputs of the NN are time, day of the year, geographical latitude, solar zenith angle, the twelve recent past observations of f0F2 (F_-23, F_-22, F_-21, F_-20, F_-19, F_-18, F_-5, F_-4, F_-3, F_-2, F_-1, F₀) and the 30-day mean moving values of f0F2 (A_-24, A_-23, A_-22, A_-4, A_-3, A_-2, A_-1, A₀) from the target location. The outputs of the NN are F₊₁, F₊₂, F₊₃, F₊₄, F₊₅, representing the values of f0F2 up to 5 h ahead. Data from Wulumqi, Changchun, Chongqing and Guangzhou stations spanning the period 1958---1968 are used for training the NN. Historical data at nine different stations in China are used to checkout the network respectively (Not including the training set). The performance of the NN is measured by calculating the Root-Mean-Square error (RMS) difference between the NN outputs and measured station data. The results indicate that the prediction of NN has good agreement with measured data. Taken into account those temporal and spatial inputs mentioned above, the NN can be applied successfully in the short-term forecasting of the ionospheric f0F2 in China.