In this paper, we approach automated seismic interference (SI) identification for towed-streamer acquisitions in a new way. We show how to teach a neural network to identify SI based on features the human brain would use for such a task. This includes describing geophysical attributes such as amplitudes and wavefield propagation directions with instantaneous and multishot statistical measures. The statistical measures are then passed to a multilayer perceptrons neural network, first for training and then validation using noise-free synthetic data. Following encouraging results on the synthetic data, we applied the neural network to identify SI on field data from the North Sea and Barents Sea without any further training. The SI in a North Sea case study was very similar to the SI used for training; whereas, the SI in a Barents Sea case study was very different in terms of moveout and amplitude. Even though the neural network was only trained on noise-free synthetics, it successfully detected the SI in both cases.