Two new entropic measures are proposed: the A-entropy and Eentropy, which are compared during competitive training processes in multiplayer networks with radial basis units. The behavior of these entropies are good indicators of the orthogonality reached in the layer representations for vector quantization tasks. The proposed E-entropy is a good candidate to be considered as a measure of the training level reached for all layers in the same training process. Both measures would serve to monitorize the competitive learning in this kind of neural model, that is usually implemented in the hidden layers of the Radial Basis Functions networks.