In this paper, we address the issues in representation of continuous valued variables by firing times of neurons in the spiking neural network used for clustering multi-variate data. The existing range-based encoding method encodes each dimension separately. This method does not make use of the correlation among the different variables, and the knowledge of the distribution of data. We propose a region-based encoding method that places multi-dimensional Gaussian receptive fields in the data-inhabited regions, and captures the correlation among the variables. Effectiveness of the proposed encoding method in clustering the complex 2-dimensional and 3-dimensional data sets is demonstrated. © 2008 Springer-Verlag Berlin Heidelberg.