The vast majority of GNSS users move in urban areas, where the signal conditions are highly unstable and multipath or gross errors make GNSS navigation unreliable or plainly unfeasible. In this study, features from real GNSS data collected by different grades of receivers have been compared, to find candidate statistical indicators of the context that allow the automatic recognition of open sky or obstructed environments. The considered features are all pre-PVT and snapshot-based, hence suitable for real-time applications. They are, namely: the number of visible satellites, the dilution of precision, the multipath linear combination with dual frequency measurements and the C/N0 difference between each couple of satellites in the same epoch at the same frequency. All measurements have been gathered both in open sky and in obstructed scenarios. Evidence suggests the multipath linear combination and the C/N0 difference between couples of satellites as the most promising baselines for an environment classifier based on Machine Learning.