Automated interpretation of clinical EEG recordings will reduce subjectivity and visual bias from analysis and can reduce the time required for interpretation. As a first step in the design of a fully automated system, a method is presented to characterize the main properties of the posterior dominant rhythm (PDR), in particular its frequency, symmetry and reactivity. The presented method searches for dominant peaks in the EEG spectra during eyes-closed states with a three-component curve-fitting technique. From the fitted curve, the frequency and amplitude are estimated. The symmetry and the reactivity are found using the spectral power at the PDR frequencies. In addition, a certainty value is introduced as a measure of confidence for each estimate. The method was evaluated on a test set of 1215 clinical EEG recordings and compared to the PDR frequencies obtained from the visual analysis, as reported in the diagnostic reports. The calculated PDR frequencies were within 1.2Hz of the visual estimates in 92.5% of the cases. Even higher accuracies were reached when estimates with low certainty values were discarded. The presented method quantifies essential features of the PDR with a matched accuracy to visual inspection, making it a feasible contribution to the design of a fully automated interpretation system.