In this paper, we address the problem of detecting the presence of a myocardial scar from the standard electrocardiogram (ECG)/vectorcardiogram (VCG) recordings, giving effort to develop a screening system for the early detection of the scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of the myocardial scar. Two of these methodologies are: 1) the use of a template ECG heartbeat, from records with scar absence coupled with wavelet coherence analysis and 2) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate a support vector machine classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. The classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying tenfold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%).