Commercial steels are nowadays sophisticated alloys formed by a large number of alloying elements. The martensite start ( Ms) temperature of such steels is of vital engineering importance, and its prediction through models allows us to enhance the design and development of industrial products. In the present work, Ms temperature dependence on chemical composition has been examined by neural network analysis. Neural networks represent powerful methods of non-linear regression modelling. The network is a mathematical function which is fitted to experimental data. The influence of alloying elements such as C, Mn, Si, Cr, Ni, Mo, V, Co, W, Al, Nb, Cu, B and N on Ms temperature was analysed. Finally, a new empirical equation for Ms temperature was derived based on the neural network results.

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