The objective of the present thesis research is to apply state-of-the-art experimental and data analysis techniques to the study of gas-liquid pipe flows, with a focus on conditions occurring in header-feeder systems of nuclear reactors under different accident scenarios. Novel experimental techniques have been proposed for the identification of the flow regime and measurement of the flow rates of both phases in gas-liquid flows. These techniques were automated, non-intrusive and economical, which ensured that their use would be feasible in industrial as well as laboratory settings. Measurements of differential pressure and the gas and liquid flow rates were collected in vertical upwards air-water flow at near-atmospheric pressure. It was demonstrated that the probability density function of the normalized differential pressure was indicative of the flow regime and using non-linear dimensionality reduction (the Elastic Maps Algorithm), it was possible to automate the process of identifying the flow regime from the differential pressure signal. The relationship between the probability density function and the power spectral density of normalized differential pressure with the gas and liquid flow rates in air-water pipe flow was also established and a machine learning algorithm (using Independent Component Analysis and Artificial Neural Networks) was proposed for the estimation of the phase flow rates from these properties. The proposed methods were adapted for use with single and dual conductivity wire-mesh sensors in vertical upwards and downwards air--water flows. A thorough evaluation of the performance and measurement uncertainty of wire-mesh sensors in gas-liquid flows was also performed. Lastly, measurements of the flow distribution in feeder tubes supplied with air-water mixtures by a simplified header model were collected and correlated to the observed flow patterns in the header.