In this study, the performance and exhaust emission values in a four-stroke, four-cylinder turbocharged Diesel engine fueled with ethanol-diesel fuel blends (10% and 15% involume) were investigated by using Artificial Neural Network (ANN) modeling. The actual data derived from engine test measurements was applied in model training, crossvalidation, and testing. To train the network, fuel injection pressures, throttle positions,engine speed, and ethanol fuel blend ratios were used as input layer in the network. Theoutputs are the engine performance values (engine torque, power, brake mean effectivepressure, and specific fuel consumption) and exhaust emissions (SO2, CO2, NOx, andsmoke level (N%)) which were measured in the experiments.The back-propagation learning algorithm with three different variants, a single layer,and logistic sigmoid transfer function (log-sig) was used in the network. By using theweights of the network, formulations were given for each output. The network for test datayielded the R2 values of 0.999 and the mean % errors for test data are smaller than 3.5%for the performance and 8% for the emissions.Ankara Yıldırım Beyazıt Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendşiilği Ankara, 06010, Türkiye