A new neural network (NN) based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa (33.3¿S, 26.5¿E) has been developed and is referred to as the LAM model. This paper discusses the development of the F layer contribution to the LAM model. Archived data from the Grahamstown ionospheric station have been presented to various NNs, which have been trained to predict the parameters required to produce an electron density profile. Since the dataset was only made up of Grahamstown data, the model is currently a single station model. The input space designed for the F layer contribution to the LAM model consisted of various combinations of the following parameters: day number (DN), hour (HR), a measure of the solar activity, and a measure of the magnetic activity. The solar activity input was represented by a 2-month running mean value of the sunspot number (R2), while the magnetic activity variable was represented by either a 24-hour or 48-hour running mean of the magnetic ak value (A8 or A16). The outputs from the NNs were the peak parameters and Chebyshev coefficients required to describe the shape and location of the F profile. This paper also discusses how NNs have been employed to provide an effective mechanism for determining the probability of the existence of an F1 layer. An F1 layer can exist in one of the following three states; F1 exists, no F1, or F1 exists in L condition state. A special NN was trained to provide the probability of F1 layer existence in each of these states. An L algorithm is applied to determine the shape and location of the profile under L condition state. In addition, a smoothing technique was designed to deal with discontinuities across the F1-F2 boundary. It is shown that the NN-based LAM model can successfully predict descriptions for the shape and location of the average profile for a given input set, and, in addition, that NNs can be employed to provide solutions to previously difficult prediction tasks such as the probability of F1 layer existence.