The use of neural networks (NNs) has been employed in this work to develop a global model of the ionospheric F2 region critical frequency, foF2. The main principle behind our approach has been to utilize parameters other than simple geographic coordinates, on which foF2 is known to depend, and to exploit the ability of NNs to establish and model this nonlinear relationship for predictive purposes. The foF2 data used in the training of the NNs were obtained from 59 ionospheric stations across the globe at various times from 1964 to 1986, on the basis of availability. To test the success of this approach, one NN (NN1) was trained without data from 13 stations, selected for their geographic remoteness, which could then be used to validate the predictions of the NN for those remote coordinates. These stations were subsequently included in our final NN (NN2). The input parameters consisted of day number (day of the year), universal time, solar activity, magnetic activity, geographic latitude, angle of meridian relative to subsolar point, magnetic dip angle, magnetic declination, and solar zenith angle. Comparisons between foF2 values determined using NNs and the International Reference Ionosphere (IRI) model (from Union Radio Scientifique Internationale (URSI) and International Radio Consultative Committee (CCIR) coefficients) with observed values are given with their root-mean-square (RMS) error differences for test stations. The results from NN2 are used to produce the global behavior of hourly values of foF2 and are compared with the IRI model using URSI and CCIR coefficients. The results obtained (i.e., RMS error differences), which compare favorably with the IRI models, justify this technique for global foF2 modeling.