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Thermal cracking of hydrocarbons converts them into valuable materials in the petrochemical industries. Multiplicity of the reaction routes and complexity of the mathematical approach has led us use a kind of black-box modelingsartificial neural networks. Reactor feed type plays an essential role on the product qualities. Feed type is a qualitative character. In this paper, a method is presented to introduce a range of petroleum fractions to the neural network. To introduce petroleum cuts with final boiling points of 865 °F maximum to the neural network, a real component substitute mixture is made from the original mixture. Such substitute mixture is fully defined, it has a chemical...