Characterization and modeling of ionospheric variability in space and time is very important for communications and navigation. To characterize the variations, the ionosphere should be monitored, and the sparsity of the measurements has to be compensated by interpolation algorithms. The total electron content (TEC) is a major parameter that can be used to obtain regional ionospheric maps. In this study, neural networks (NNs), specifically multilayer perceptrons (MLPs) and radial basis function networks (RBFN), are investigated for the merits of their nonlinear modeling capability. In order to assess the performance of MLP and RBFN structures with respect to mapping and ionospheric parameters, these algorithms are applied to synthetically generated TEC surfaces representing various ionospheric states. Synthetic TEC data are sampled homogenously and randomly for a varying number of data points. The reconstruction errors show that the performance improves significantly when homogenous sampling is preferred to random station distribution. The best MLP and RBFN structures for any possible realistic scenario are determined by examining the performance parameters for all possible cases. It is also observed that RBFN with local receptive fields relies more on the number of training data points. In contrast to RBFN, MLP as a global approximator depends strongly on ionospheric trends. Finally, chosen MLP and RBFN models are applied to a set of real GPS-TEC values obtained from central Europe, and their performances are compared with well known Global Ionospheric Maps produced by the International GNSS Service. The resolution and interpolation quality of the generated maps indicate that NNs offer a powerful and reliable alternative to the conventional TEC mapping algorithms.