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Two-phase flows are commonly found in many industrial processes. Numerous theoretical and experimental studies have been carried out and reported in literature for modeling of two-phase flow. However, because of the inherent complexity of two-phase flow, it is still a challenge to make accurate predictions for the different parameters of two-phase flow such as flow pattern, in-situ phase fraction, pressure drop and heat transfer coefficient. Artificial neural network (ANN) technique has been proposed as a powerful and computational tool for modeling and solving complex problems that cannot be described with simple mathematical models. This book consists of three chapters. The first chapter presents a review of gas-liquid and liquid-liquid two-phase flows in horizontal, vertical and inclined pipes. In the second chapter, an overview about the most commonly used network architectures in the field of two-phase flow is provided. Finally, in the third chapter, some examples of artificial neural network applications in the field of two-phase flow are presented.