Ventricular tachycardia (VT) is a life-threatening arrhythmia that is commonly treated by catheter ablation guided by substrate mapping. This procedure relies on the visual inspection of intracardiac electrograms (EGMs) by the cardiologist, to identify arrhythmogenic sites. This task can be challenging, because of the huge amount of data to interpret. To address this issue, we proposed for the first time a method for the discrimination between physiological and anomalous bipolar EGMs based on Siamese neural networks (SNN), able to deal with small datasets, for the automatic labelling of the EGMs on the map. On a balanced dataset of 1504 physiological and anomalous EGMs from nine post-ischemic VT patients, we demonstrated that a SNN trained to distinguish between the two types of EGMs is able to achieve a high degree of specificity (91.0±3.2 %) and sensitivity (92.7±3.4 %). Potentially, the proposed approach could also be exploited to map the similarity of the EGMs, resulting in a novel electroanatomic map for the identification of areas of abnormal conduction.