Abstract: In this study, we apply the wavelet transform and the fusion of two Bayesian neural networks to design an electrocardiogram (ECG) beat classification system; our main objective is to minimize at the maximum the miss diagnostic. Six ECG beat types namely: Normal beat (N), Left Bundle Branch Bloc (LBBB), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature contraction (APB) and the Paced Beat (PB), obtained from the MIT-BIH ECG database were considered. First, five Discrete Wavelet Transform (DWT) levels were applied to decompose each ECG signal beat (360 samples centered on the peak R) into time-frequency representations. Statistical features relative to the three last decomposed signals and the last approximation, in addition to the original signal, were then calculated to characterize the ECG beat. Secondly, the fusion of RBFNN (Radial Basis Functions Neural Network) and BPNN (Back Propagation Neural Network) was employed as the classification system using as inputs, the calculated statistical features, in addition to the instantaneous RR interval. The proposed method achieves an equally well recognition rate of over 98% throughout all ECG beats type. These observations prove that the proposed beat classifier is very reliable and that it may be a useful practical tool for the automatic detection of heart diseases based on ECG signals.