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Successful recognition has been known to produce distinct patterns of neural activity. Many studies have used spectral power or event-related potentials of single recognition-specific regions as classification features. However, this does not accurately reflect the mechanisms behind recognition, in that recognition requires multiple brain regions to work together. Hence, classification accuracy of subsequent memory performance could be improved by using functional connectivity within memory-related brain networks instead of using local brain activity as classifiers. In this study, we examined electroencephalography (EEG) signals while performing a word recognition memory task. Recorded EEG signals were collected using a 32-channel cap. Connectivity measures related to the left hemispheric fronto-parietal connectivity (P3 and F3) were found to contribute to the accurate recognition of previously studied memory items. Classification of subsequent memory outcome using connectivity features revealed that the classifier with support vector machine achieved the highest classification accuracy of 86.79 ± 5.93% (mean ± standard deviation) by using theta (3–8 Hz) connectivity during successful recognition trials. The results strongly suggest that highly accurate classification of subsequent memory outcome can be achieved by using single-trial functional connectivity.