Currently, food processing industry is driven by several requirements. Thisrnrequirement includes ensuring safety, meeting quality standard and customerrnexpectation and reducing production cost to be competent in market. To achievernthis requirement they have to operate at optimum process conditions all the time.rnIn food processing, due to the nature of the process, it is difficult to find andrnoperate at the best conditions solely by experience.rnThe Ethiopia food industry is no coping up with such requirement due costrnof optimization and low level of education of works operating in the productionrnsystem. Thus, it is necessary modeling of the process or part of the process torncapture the relation of between important process parameters and use the model torncontrol and improve the process better. In addition, it is found necessary to makernthe model accessible for the operators working in Ethiopian industry. Usingrnartificial neural network method is found to be very good modeling to tool to solvernfood engineering problems.rnIn this thesis, therefore, artificial neural network method is used to modelrnand tested for selected food industry engineering problems, specifically, waterrnactivity prediction, predictive food microbiology and control chart patternrnrecognition. The model is enclosed in interactive software so that it could also bernused by people that do not have sophisticated mathematical and technical skills.rnThe result obtained for all problems shows that neural network modeling can bernused to model food process and to predict food process parameters with sufficientrnaccuracy.