Automatic identification and extraction of commercial blocks in telecast news videos find a lot of applications in the domain of broadcast monitoring. Existing works in this domain have used channel specific assumptions, machine learning techniques and frequentist approaches for detecting commercial video segments. We note that in the Indian context, several channel specific assumptions do not hold and often news and commercials have comparable frequencies of occurrence. This motivates us to use the machine learning techniques for classifying commercials in news videos. Our main contribution lies in the proposal of two features which are shown to outperform the existing audio-visual featuresfirst, the MFCC bag of words (BoW) as audio track feature and second, overlaid text distribution as video shot feature. The shot feature space is further extended by appending contextual features which are categorized by SVM based classifiers. Additionally, we have used a post-processing stage to suppress the false positives. We have experimented with 54 hours of video acquired from three different Indian English based news channels and have obtained a F-measure of around 97%.