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Numerous techniques have been used to identify flow regimes and liquid holdup in horizontal multiphase flow, but often neither perform well nor very accurate. Recently, neuro-fuzzy inference systems learning scheme have been gaining popularity in its capability for solving both prediction and classification problems. It is a hybrid intelligent systems scheme that is able to forecast an output in the uncertainty situations. This paper investigates the capabilities of neuro-fuzzy TypeI in identifying flow regimes and forecasting liquid holdup in horizontal multiphase flow. The performance of neuro-fuzzy modeling scheme is implemented using different real-world industry databases. Comparative studies were carried out to compare neuro-fuzzy systems performance with the most popular existing approaches in identifying flow regimes and predict liquid holdup in horizontal multiphase flow. Results show that neuro-fuzzy is …