An accurate porosity index prediction is one of the most important requirements in the oil industry. But preparing and gathering of information is very time consuming and expensive and the evaluating process needs highly skilled experts. Therefore, it is very economical to design a model that can predict porosity with good accuracy, which takes less time and cost. In this study, about 10,000 accredited log data and evaluated porosity (obtained by multimin model), which are related to one of the southern fields of Iran, are available. Optimal neural networks structures for predicting porosity index have been investigated. At first, neutron, sonic, and density logs were used as input variables. Half of the data was used as a training set and other data were used as a test data set. By implementing various radial basis function networks the best structure was determined and ultimately porosity index was predicted with a high percentage of precision.