Purpose: Current substrate based ablation strategies for ventricular tachycardia (VT) advocate targeting all excitable abnormal ventricular electrograms within the scar. However only a fraction of these electrograms populate the VT supporting channels. We hypothesized that the electrogram properties in the standard model can identify information content specific to the location of VT supporting channels and lead intent and focused substrate ablation. Methods: Study was conducted in three stages. Firstly, patients with ischemic cardiomyopathy (ICM; n=16) and multiple inducible VT (n=58) undergoing catheter ablation were evaluated. Left ventricular endocardial mapping was performed with high-density PentaRay™ catheter and NavX™ system. VT channel was defined as series of matching pacemaps with stimulus to QRS interval ≥40ms. Entrainment mapping confirmed pace map findings whenever feasible. In the blinded second stage, the standard model was applied to all SR maps. The timing of local activation was determined as mean of activation times of all electrogram peak deflections, and dispersion was quantified by their standard deviation. Shannon entropy (ShEn) was calculated as an index of electrogram amplitude distribution. A VT channel region was identified in the model by assemblage of node of latest mean activation and proximate low ShEn in a zone of high dispersion with adjoining interface of low dispersion. Channel locations built on this model were compared with VT channels developed in the first stage. Finally, for the test of concept, performance of this model was examined prospectively in 3 additional ICM patients to lead catheter ablation. Results: Mean 763±203 sampling points were taken, 451±145 points in dense scar (≤0.5mV). From 1770 pacemaps, 174 channels were identified, 47 corresponded to inducible VTs. Of all the fractionated (mean 114±85), late (mean10±5) and very-late (mean 3±3) potentials, only 18%, 23% and 35%, respectively were recorded in the VT channels. Channels built on the model had high agreement with VT channels [κ =0.89, 95% CI 0.84 to 0.94] with high sensitivity (86%, 95% CI 76 to 93), specificity (100%, 95% CI 99.8 to 100), positive (93%, 95% CI 84 to 98) and negative (100%, 95% CI 99 to 100) predictive value for localizing an inducible VT channel. Finally, focused ablation in 3 patients within the postulated channel regions eliminated 6/8 inducible VTs. Conclusion: The standard model can hasten identification of VT channels compared to other frequently employed electrogram characteristics. This will reduce the difficulties in the current substrate based ablation strategies.