In this paper, we have considered a real life scenario where data is available in blocks over the period of time. We have developed a dynamic cluster based ensemble of classifiers for the problem. We have applied clustering algorithm on the block of data available at that time and have trained a neural network for each of the clusters. The performance of the network is tested against the next available block of data and based on that performance the parameters of the clustering algorithm is changed at runtime. In our approach increasing the number of clusters is considered as changing of the parameter settings. The misclassified instances of the test data are also joined with the training data to refine the knowledge of the classifiers. The proposed system is capable of identifying the decision boundary of different classes based on the current block of data more precisely. An extensive experiments has been performed to evaluate this dynamic system and to compute the optimal parameters of the proposed procedure.