Subglacial lakes decouple the ice sheet from the underlying bedrock, thus facilitating the sliding of the ice masses towards the borders of the continents, consequently raising the sea level. This motivated increasing attention in the detection of subglacial lakes. So far, about 70% of the total number of subglacial lakes in Antarctica have been detected by analysing radargrams acquired by radar sounder (RS) instruments. Although the amount of radargrams is expected to drastically increase, from both airborne and possible future Earth observation RS missions, currently the main approach to the detection of subglacial lakes in radargrams is by visual interpretation. This approach is subjective and extremely time consuming, thus difficult to apply to a large amount of radargrams. In order to address the limitations of the visual interpretation and to assist glaciologists in better understanding the relationship between the subglacial environment and the climate system, in this paper, we propose a technique for the automatic detection of subglacial lakes. The main contribution of the proposed technique is the extraction of features for discriminating between lake and non-lake basal interfaces. In particular, we propose the extraction of features that locally capture the topography of the basal interface, the shape and the correlation of the basal waveforms. Then, the extracted features are given as input to a supervised binary classifier based on Support Vector Machine to perform the automatic subglacial lake detection. The effectiveness of the proposed method is proven both quantitatively and qualitatively by applying it to a large dataset acquired in East Antarctica by the MultiChannel Coherent Radar Depth Sounder.