Vectorcardiograpic (VCG) parameters can supplement the diagnostic information of the 12-lead electrocardiogram (ECG). Nevertheless, the VCG is seldom recorded in modern-day practice. A common approach today is to derive the Frank VCG from the standard 12-lead ECG (distal limb electrode positions). There is, to date no direct method that allows for a transformation from 12-lead ECGs with proximal limb electrode positions (Mason-Likar (ML) 12-lead ECG), to Frank VCGs. In this research, we develop such a transformation (ML2VCG) by means of multivariate linear regression on a training data set of 545 ML 12-lead ECGs and corresponding Frank VCGs that were both extracted surface potential maps (BSPMs). We compare the performance of the ML2VCG method against an alternative approach (2step method) that utilizes two existing transformations that are applied consecutively (ML 12-lead ECG to standard 12-lead ECG and subsequently to Frank VCG). We quantify the performance of ML2VCG and 2 step on an unseen test dataset (181 ML 12-lead ECGs and corresponding Frank VCGs again extracted from BSPMs) through root mean squared error (RMSE) values, calculated over the QRST, between actual and transformed Frank leads. The ML2VCG transformation achieved a reduction of the median RMSE values for leads X (13.9μV; p