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Neural Networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, Artificial Neural Networks (ANNs) are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their importance in this field is growing. Also, it is the Artificial Intelligence Technique that deals best with uncertainly. The present work aims to analyze the neural networks for financial time series forecasting. Specifically the ability to predict future trends of North American, European and Brazilian Stock Markets. Accuracy is compared against a traditional forecasting method, generalized autoregressive conditional heteroscedasticity (GARCH). Furthermore, it is examined the best choice of network design for each sample of data. It was concluded that ANNs do have the capability to forecast the stock markets studied and, if properly trained, can improve the robustness according to the network structure.