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This paper explores the applicability of neural networks (NN) for forecasting meteotsunamis affecting Ciutadella harbour (Menorca, Balearic Islands, Spain). Virtually every year, Ciutadella suffers meteotsunamis with wave heights (crest-to-trough difference in about 5-min interval) around 1 m, and at several episodes in its modern history, the waves have reached 2–4 m. A timely and skilled prediction of these phenomena could significantly help to mitigate the damages inflicted to the port facilities and the moored vessels. Once properly trained, a NN is a computationally cheap forecasting method; the approach could be easily incorporated by civil services which are responsible for issuing warnings and organizing a prompt response. We examine the relevant physical mechanisms that promote meteotsunamis in Ciutadella harbour and choose the input variables of the NN accordingly. Two different NNs are devised and tested: a dry and wet scheme. The difference between schemes resides on the input layer, while the first scheme is exclusively focused on the triggering role of atmospheric gravity waves (governed by temperature and wind profiles across the tropospheric column), the second scheme also incorporates humidity as input information with the purpose of accounting for the occasional influence of moist convection. We train both NNs using the resilient backpropagation with weight backtracking method. Their performance is tested by means of classical verification indexes. We also compare both NN results against the performance of a substantially different prognostic method that relies on a sequence of atmospheric and oceanic numerical simulations (TRAM-rissaga method). The new prediction systems work fairly well in distinguishing rissaga and non-rissaga situations, even though they tend to underestimate the amplitude of the harbour oscillation. Both NN schemes show a skill comparable to that of computationally expensive approaches based on direct numerical simulation of the physical mechanisms. The expected greater versatility of the wet scheme over the dry scheme cannot be clearly proved owing to the limited size of the training database, which lacks a sufficient number of convectively driven rissaga events. The results emphasize the potential of a NN approach and open a clear path to an operational implementation using the whole database for training, avoiding the limitations derived from splitting the available list of events into training and testing subsets. [ABSTRACT FROM AUTHOR]