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A novel approach for the classification of compressed video data using centroid neural network with Bhattacharyya kernel (CNN(BK)) is proposed in this paper. The proposed classifier is based on centroid neural network (CNN) and also exploits advantages of the kernel method for mapping input data into a higher dimensional feature space. Furthermore, since the feature vectors of compressed video data are modelled by Gaussian probability density function (GPDF), the classification procedure is performed by considering Bhattacharyya distance as the distance measure of the proposed classifier. Experiments and results on a video trace data demonstrate that the proposed classification scheme based on CNN (BK) outperforms conventional algorithms including self-organizing map (SOM) and conventional CNN.