This research examines and analyzes the use of Artificial Neural Networks (ANN) asa forecasting tool. Specifically a neural network's ability to predict future trends ofinflation is tested. Accuracy is compared against a traditional forecasting method,multiple linear regression analysis. Finally, the probability of the model's forecastbeing correct is calculated using conditional probabilities. While only brieflydiscussing neural network theory, this research determines the feasibility andpracticality of using neural networks as a forecasting tool for inflation in Indonesia.This study builds upon the work done by Edward Gately in his book Neural Networksfor Financial Forecasting. This research validates the work of Gately and describesthe development of a neural network that achieved an 86 percent probability ofpredicting an inflation rise, while multiple regression analysis is only to predictinflation that achieved a 16%. It was concluded that neural networks do have thecapability to forecast inflation and, if properly trained, we could benefit from the useof this forecasting tool.