This paper advances the methodology of hybrid classification architectures for face and hand gesture recognition tasks and shows their feasibility through experimental studies using the FERET data base and gesture images. The hybrid architecture, consisting of an ensemble of connectionist networks - radial basis functions (RBF) - and inductive decision trees (DT), combines the merits of 'holistic' template matching with those of 'abstractive' matching using discrete features and subject to both positive and negative learning. The hybrid architecture, quite general as it applies to both face and hand gesture recognition, derives its robustness from (i) consensus using ensembles of RBF networks, and (ii) flexible matching using categorical classification via decision trees. The experimental results, proving the feasibility of our approach, yield (i) 93 % accuracy, using cross validation, for contents-based image retrieval (CBIR) subject to correct ID matching tasks, such as 'find Joe Smith with/without glasses', on a data base of 200 images, and (ii) 96 % accuracy, using cross validation, for forensic verification on a data base consisting of 904 images corresponding to 350 subjects (of whom 102 are duplicates). Cross validation results on the hand gesture recognition task yield a false negative rate of 3.6 % and a false positive rate of 1.8 % , using a data base of 750 images corresponding to 25 hand gestures.