Radial Basis Function (RBF) classifiers, which consist of an hidden and an output layer, are traditionally trained with a two-stage hybrid learning approach. This approach combines an unsupervised (datadriven) first stage to adapt RBF hidden layer parameters with a supervised (error-driven) second stage to learn RBF output weights. Several simple strategies that exploit labeled data in the adaptation of centers and spread parameters of RBF hidden units may be pursued. Some of these strategies have been shown to reduce traditional weaknesses of RBF classificaton, while typical advantages are maintained, e.g., fast training, easy implementation, low responses to outliers. In this work, we compare a traditional RBF two-stage hybrid learning procedure with an RBF two-stage learning technique exploiting labeled data to adapt hidden unit parameters. Two data sets were analized: the first consisted of multitemporal remote sensed data; the second consisted of Magnetic Resonance images...