ENSO, El Nino southern-oscillation, is known to be the strongest climatic variation on seasonal to inter-annual time scales. It causes severe droughts, floods, fires, and hurricanes leading to economical disasters. Hence, ENSO prediction is crucial to help in preserving world economy. This study explores the use of relatively simple inputs in developing an artificial neural networks (ANN) model for predicting ENSO, identifying the best ENSO indicator for neural networks, and assessing the impact of different time steps on the model efficiency. Two types of ENSO indicators, Southern Oscillation Index (SOI) and NiñoS, were used to develop neural networks models in this study. The use of SOI as an ENSO indicator performed much better compared to NiñoS. ANN model using a monthly time span proved to have a high potential of success. The devised monthly model was a three-layer multi-layer perceptron model trained with error backpropagation algorithm using the logistic activation function for hidden and output layers. The model showed high stability due to the similar R2values when the training and testing data patterns in the Input data sets were swapped.