This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a data set at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult–to–classify patterns through clustering and achievement of diversity through layering leads to better classification results as evidenced from the experimental results.