The objective of this study is to investigate experimentally the relationship between an operator's mental workload and the information flow rate of accident diagnosis tasks and further to propose the information flow rate as an analytic method for measuring the mental workload. There are two types of mental workload in the advanced main control room of nuclear power plants: the information processing workload, which is the processing that the human operator must actually perform in order to complete the diagnosis task, and emotional stress workload experienced by the operator. In this study, the focus is on the former. Three kinds of methods are compared to measure the operator's workload: information flow rate, subjective methods, and physiological measures. Information flows for eight accident diagnosis tasks are modeled qualitatively using a stage model and are quantified using Conant's model. The information flow rate is obtained by imposing time limit restrictions for the tasks. National Aeronautics and Space Administration-Task Load Index (NASA-TLX) and Modified Cooper-Harper (MCH) scale are selected as subjective methods. For the physiological measurements, an eye tracking system analyzes eye movements related to the operator's blinking and fixation on regions of interests. Through the experiments, the relationship between the information flow rate of accident diagnosis tasks and the selected measures is investigated. Results indicate that the information flow rate of diagnosis tasks is in high agreement with both subjective rating scores and eye movement parameters related to blinking and fixation on the regions of interest. It appears, then, that information flow rate can be an alternative as an analytic approach for measuring mental workload. By using data on the information flow rate, we can predict the mental workload required for a task without performing experiments in advance.