Prior to an earthquake, there is energy storage in the seismogenic area, the release of which results in a number of micro-cracks, which in effect produce a weak electric signal. Initially, there is a rapid rise in the number of propagating cracks, which creates a transient electric field. The whole process lasts in the order of several tens of minutes, and the resulting electric signal is considered as an electric earthquake precursor (EEP). Electric earthquake precursor recognition is mainly prevented by the very essence of the signal itself. The nature of the signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth's electric field prior to an earthquake, often unobservable within the severely stronger embedded in noise electric background. Furthermore, EEP signals vary in terms of duration and size making reliable recognition even more difficult. The work described in this thesis incorporates neuro-fuzzy technology for the reliable recognition of EEP signals within the electric field. Neuro-fuzzy networks are neural networks with intrinsic fuzzy logic abilities, i.e. the weights of the neurons in the network define the premise and consequent parameters of a fuzzy inference system. In particular, the adaptive neuro-fuzzy inference system (ANFIS) is used, which has been shown to be effective as a universal approximator that can match any input/output data set, providing the system is adequately trained. An average model for EEP signals has been identified based on a time function describing the evolution of the number of propagating cracks. Pattern recognition is performed by the neural network to identify the average EEP model from within the electric field. The fuzzy nature of the neuro-fuzzy model, though, enables the network to classify as EEPs, signals that are not exactly the same but do approximate the average EEP model. On the other hand, signals that look like EEPs but do not approximate enough the average model are being suppressed preventing false classification. The effectiveness of the proposed network is demonstrated using electrotelluric data recorded in NW Greece in 1995. Following training, testing with unseen data verifies the reliable performance of the model.