and Li ,2008), ART1(Yang and Yang,2008;Carpenter and Grossberg, 1987), competitive learning rule (Malave and Ramachandran,1991), Kohonen's self-organizing feature maps 
(Venkumar P and Haq AN, 2006a,2006b), Fuzzy ART neural network(Suresh et al.,1999).

Recently, in most neural network models, competitive learning algorithm (Malave and Ramachandran, 1991) and SOM (chattopadhyay *et al*., 2009; Guerrero *et al*., 2002; Venugopal and Narendran, 1994) has been applied in GT. Since the competitive learning network is an unsupervised approach, it is very suitable for use in GT as the cell formation is a NP complete problem. The robustness of those SOM applications allows it to handle the data visualisation and classification effectively which provides the motivation in applying this approach for exploring into the analysis of the data problem.

In this paper, an attempt is made to use the binary part-machine matrices which are obtained from artificially generated (problem\#1 in table 1) and literature (Table 3) to group the parts into part families and machines into machine cells with an idea to maximize the proposed performance measure by introducing a cluster analysis approach using the self organizing map (SOM) proposed by Kohonen (2001). The SOM is a nonlinear statistical technique for transforming and visualising multi-dimensional data in a lower-dimensional map (Kohonen, 1998; Mancuso, 2001). The SOM technique based solutions have been designed for problems involving visualisation and cluster analysis (Flexer, 2001; Kiang, 2001) and implemented for many applications for exploratory data analysis. SOM clustering with color coding is a way to group data, according to its properties (Kaski and Kohonen, 1998; Kaski, 2001). The SOM is a competitive learning neural network model, which preserve the distribution and topology information of input data in a low dimension map grid. After mapping, the preserved information can be extracted from which many valuable characteristics of original data can be obtained, such as distribution, cluster, component correlation etc. The novelty of this proposed approach is that the clustering of machine part cell formation can be visually decipherable and thereby cell formation can be studied in depth in a simple and robust way. All experiments, in the present study were performed in the MATLAB programming language using the SOM MATLAB Toolbox (Vesanto et al., 2000).

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