Ozone is one of the most important constituents of the Earth's atmosphere. Ozone is vital because it maintains the thermal structure of the atmosphere. However, exposure to high concentrations of Ozone can cause serious problems to human health, vegetation, and damage to surfaces. The complexity of the relationship between the main attributes that severely affect surface ozone levels have made the problem of predicting its concentration very challenging. Innovative mathematical modeling techniques are urgently needed to get a better understanding of the dynamics of these attributes. In this paper, prediction of the surface ozone layer problem is investigated. A comparison between two types of artificial neural networks (ANN) (multilayer perceptron trained with backpropagation and radial basis functions (RBF) networks) for short prediction of surface ozone is conclusively demonstrated. Two models that predict the expected values of the surface ozone based on three variables (i.e. nitrogen-di-oxide, temperature, and relative humidity) are developed and compared.