In this thesis the application of feed forward type artificial neural networks to modelrnchemical engineering processes are demonstrated with reference to five differentrnproblems.rnNeural network models are constructed and employed to predict vapor-liquid equilibriumrn(VLE) data of twelve different binary systems having different chemical structures andrnsolution types (azeotrope-nonazeotrope) in various conditions (isothermal or isobaric). Itrnis observed that the data found by neural network model gives an excellent agreementrnwith the experimental data. In fact the neural network model can be treated as a powerfulrnmeans for VLE data prediction in a fast and reliable way.rnThis study has confirmed the feasibility of using a neural network to capture thernnonlinear and interacting relationships between the moisture content and different dryingrnconditions of potato. Simulating time series temperature profiles of adiabatic batchrnreactor has also investigated. Neural network trained with a limited number ofrnexperimental data were capable of predicting fresh data that were not used to train thernnetwork. The results obtained in using the developed models are physically sound asrnexpected from experience.rnSimulating a human operator controlling a chemical plant is also a good instance wherernthe advantage of using artificial neural networks is demonstrated in the thesis. This thesisrnalso describes the use of multilayer feed forward neural networks as a CO2 analyzer. Itrnwas proved that MLP-type network of a relatively simple structure made it possible tornpredict the CO2 effluent from a furnace.rnTaking in to account the difficulties in experimental conditions, complicatedrnmeasurements and unavoidable errors of devices used, limit the precision of laboratoryrnmeasurement results. The accuracy of the results generated by the developed neuralrnnetwork models may be considered satisfactory for engineering calculations.