While detection of acute cardiac disorders such as myocardial infarction (MI) from electrocardiogram (ECG) and vectorcardiogram (VCG) has been widely reported, identification of MI locations from these signals, pivotal for timely therapeutic and prognostic interventions, remains a standing issue. We present an approach for MI localization based on representing complex spatiotemporal patterns of cardiac dynamics as a random-walk network reconstructed from the evolution of VCG signals across a 3-D state space. Extensive tests with signals from the PTB database of the PhysioNet databank suggest that locations of MI can be determined accurately (sensitivity of ∼88% and specificity of ∼92%) from tracking certain consistently estimated invariants of this random-walk representation.