Identifying the correct location of channel heads remains a challenging aspect in hydrogeomorphic analysis. Though field mapping is a reliable method, this may become infeasible for large basins. High resolution remote sensing data provides another way to predict channel heads. Existing literatures have suggested use of digital elevation models (DEM) to extract channel heads by applying an area or slope-area thresholding method. However, channel initiation process is more complex and depends on other factors like topographic curvature, land use land cover etc. In this study, we have used machine learning models to extract channel heads from freely available 1 arc second SRTM DEM data for a basin in the Lesser Himalaya. We have used upstream area, local slope and local curvature as features in our models. Actual channel heads were digitized manually from high-resolution (1-2 m) IKONOS imagery available on Google Earth. Decision tree model generated the best results with a F1-score of 0.45 and correctly predicted around 78% of the channel heads from the test set along with a high number of false positives. Future work will be applying this method on available high-resolution Lidar-derived DEM, with more field-mapped channel heads.