Mine fire due to spontaneous combustion in coal mines is a global concern. This leads to serious environmental and safety hazards and considerable economic losses. Therefore, it is essential to assess and classify the coal seams with respect to their proneness to spontaneous combustion to plan the production, storage and transportation capabilities in mines. This paper presents a fuzzy c-mean approach for classification of coal seams based on their proneness to spontaneous combustion. This produces sets of non-exclusive clusters that allow coal samples to have memberships in multiple clusters, rather than only in exclusive partitiobns, as generated by hierarchical and k means clustering. In this research work, fifty one coal samples of varying ranks belonging to both high and low susceptibility to spontaneous combustion have been collected from all the major coalfields of India. Using moisture, volatile matter, ash content and crossing point temperature of the coal samples as the parameters, the proposed algorithm has been applied to classify the coal seams into three different categories. This classification will be useful for the planners and field engineers for taking ameliorative measures in advance for preventing the occurrence of mine fires. (C) 2012 Elsevier B.V. All rights reserved.