The application of ANN architecture varies, depending on specific investigation as desired. The **data** are processed by ANN selection, number of neurons in the hidden layer and transfer functions. Kamyar et al. successfully researched the effect of operational parameters on CRDI engine using backpropagation network with Bayesaian training algorithm [19]. For Similar applications, in order to attain accurate results, a modified network was developed by Togun and Baysec. Other researchers are fruitfully developed a model for segregation of **data** based on the physical processes, to attain accurate ANN predictions for measurement of cylinder pressure [18]. Harun Mohamed Ismail et al. modeled a diesel engine powered with blends of biodiesel to predict engine output response using back-propagation feed forward ANN. The Tansig / Purelin transfer functions, TRAINLM training algorithm is used with 10 neuron, to predict the correlations between engine control parameters and engine output responses [20]. Sayin and Ertunc applied ANN for modeling the parameters of spark ignition engine such as brake specific fuel consumption, thermal efficiency, burnt gas temperature and emissions. They found that maximum MRE reached for whole set of training **data** and test **data** was within 2 to 7% [21].