This dissertation investigates the use of Artificial Neural Nerworks (ANNs) in time series forecastig, especially financial time series, which are typically noisy and with no apparent periodicity. The dissertation covers four major parts: the study of Artificial Neural Networks and time series; the desing of ANNs applied to time series forecasting; the development of a simulation enironment; and a case study. The first part of this dissertation involved the study of Artficial Neural Netwrks and time series theory, resulting in an overview of ANNs utilization in time series forecasting. This overview confirmed the predominance of Backpropagations as the training algorithm, as well as the employment of statistical models, such as regression and moving average, for the Neural Network evaluation. In the design of ANNS, three performance measures were considered: covergence, generalization and scalability. To control these parameters, the following methods were applied: choice of activation function - sigmoid or hiperbolic tangent; choice of cost function - MSE (Mean Square Error) or MAD (Mean Absolute Deviation); choise of parameteres for controlling the gradiente descendent and learning times - the learning rate and momentum term; and network architecture. The simulation environment was developed in C language, with 3,600 lines of code distributed in three main modules: the user interface, the simulaton and the support functions modules. The user interface module is responsaible for the network configuration and for the graphical visualization. The simulation module performs the training and testing of ANNs. The support functions module takes care of the pre and pos processin, the files management and the metrics calculation. The case study concerned with the designing of an ANN to forescast the gold price in the international market. Two kinds of prediction were used: univariate - single and multi-step, and multivariate. The metrics used to evaluate the ANN performance were: U of Theil`s coeficient, MSE (Mean Square Error), NRMSE (Normalized Mean Saquare Error), POCID (Percentage Of Cnage In Direction), scattergram and graphical comparison. The results were also comapred with the Box-Jenkins model, confirming the superiority of ANN in handling non-linear and noisy data.