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The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of atrial fibrillation (AF). In order to use the ECG as a tool for the analysis of AF, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, statistical signal processing techniques, like independent component analysis (ICA) algorithms, are able to perform a multilead statistical analysis with the aim to obtain the AA. On the other hand time-domain based techniques, like Average Beat Substraction (ABS), have been well accepted and used in clinical applications to cancel out the QRS complex and the T wave. In this contribution, a QRST cancellation method based on a radial basis function (RBF) network is pro posed. Average Results for the RBF method applied are (mean±std)Cros-Correlation=0.95±0.02l and MSE = 0.356 ± 0.102 in contrast to traditional compared methods that, for the best case, yielded CC = 0.86 ± 0.031 and MSE = 0.491 ± 0.213. The results prove that RBF based methods are able to obtain a very accurate reduction of ventricular activity (VA), thus providing high quality atrial activity extraction in AF recordings.