Wavelet Neural Networks (WNNs) can be understood as neural structures which employ a wavelet layer to perform an adaptive feature extraction in the time-frequency domain. This paper aims at providing some new insight into this emerging field, discussing basic concepts involved and also detailing aspects of training and initialization. Two modifications to the basic training algorithms are also proposed, namely the introduction of a bias component in the wavelets and the adoption of a weight decay policy. For illustration, a WNN is employed in a problem of ECG segment classification.