The paper deals with the goal of component fraction estimation in multi-component flows, a critical measurement in many process systems. Electrical Capacitance Tomography (ECT) is an attractive sensing technique for this task, due to its low- cost, non-intrusion and fast response. However, typical systems, which include practicable real-time reconstruction algorithms have shown to give inaccurate results and the existing approaches to direct component fraction measurement have a performance that is typically flow-regime dependent, and they fail to discriminate fractions in three-component flows. Such systems also depend upon an intermediate image that must be interpreted to yield useful plant data. In the investigation described, an artificial neural network approach has been used to directly estimate the component fractions in gas-oil, gas- water and gas-oil-water flows from ECT measurements. A two-dimensional finite-element electric field model of a 12- electrode ECT sensor has been used to simulate measurements in stratified, annular and bubble-flow conditions. The singular-value decomposition has been used to reduce the raw measurement data to a mutually independent set. Multi-Layer Feed-Forward Neural Networks (MLFFNNs) have been trained with sets of such reduced ECT data with their corresponding component fractions. The trained MLFFNNs have been tested with test patterns consisting of unlearned ECT data. The paper reviews results of the best-trained networks that give a mean absolute error of less than 1% for the estimation of various multi-component fractions. The MLFFNNs’ estimations are also compared with a direct ECT method proposed in one of the previous works. The direct ECT method gives larger mean absolute errors than the MLFFNNs, demonstrating that artificial neural systems provide more accurate component fraction estimations.