Myocardial infarction (MI), more commonly known as heart attack, is a predominant cause of mortality all over the world. Automated MI identification techniques aid in early detection, thus ensuring timely medication and prevention. The vector-cardiogram (VCG) proves to be a more informative and low dimensional alternative for the 12 lead Electrocardiogram (ECG). The automated VCG analysis tools, reported till date, utilize a large number of features based on the sizes, area and orientation of the QRS and the T loops. Such features are not only difficult to extract but also suffers from the curse of dimensionality. This paper proposes a novel VCG feature - the volume ratio of the 3-d QRS and the ST-T loop, which combines both the loop morphologies into a single feature. Statistical analysis of this feature extracted from the PTB diagnostic ECG database reveals that it is significantly different for the healthy and infarction data and provides a MI detection sensitivity of 98.8%. This study is indicative of the strong utility of this new feature for automated MI classification algorithms. © Springer Nature Switzerland AG 2019.