In this paper, we propose a novel approach for combining the outputs of multiple neural network classifiers to reach a unified decision with improved performance in terms of higher recognition/classification rates. Our architecture consists of an ensemble of connectionist networks - radial basis functions (RBF) - and inductive decision trees (DT). The specific characteristics of our architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, (b) categorical classifications using decision trees, (c) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experiments proving the feasibility of our architecture were performed on face recognition using 900 images, gender and ethnic classification tasks using 3000 images from the FERET facial database. Specifically, we observe that a small number of networks (two or three) were sufficient in yielding a much improved classification rate as opposed to using a single RBF network or an ensemble of RBF networks employing a voting scheme.