Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed from data collected from ISO 8178-4 (1996) test cycle B-type tests (ISO) and an expanded set of tests (EXP) to predict hydrocarbon (HC) emissions from a diesel engine. LMR using the ISO training data (R 2 = 0.94) resulted in overfitting of the model as applied to the evaluation data (R 2 = 0.49). LMR based on the expanded data (R 2 = 0.68) was a better LMR model when applied to the evaluation data (R 2 = 0.64). NPN using the expanded training data (R 2 = 0.99) resulted in the best model when applied to the evaluation data (R 2 = 0.98) and is preferred for predicting HC when the larger set of test mode data are available. NPN using the ISO training data (R 2 = 0.99) resulted in a satisfactory fit for the evaluation data (R 2 = 0.91), although with a higher average absolute error (0.52 vs. 0.42 g/kWh) than NPN using the EXP training data. This model was also considered suitable for predicting HC. Results of this initial study suggest that data could be collected during ISO 8178-4 emission tests and modeled with NPN to predict HC emissions for a diesel engine operating at various constant speeds and loads.