Wireless Sensor Networks are increasingly employed as unobtrusive and infrastructureless networks in both indoor and outdoor environments. In order to reach their full potential, a number of key issues, such as localization and topology control, need to be addressed. However, the performance of these protocols is significantly impacted by the assumptions made about the underlying physical layer. Realistic radio propagation models, used as the physical layer models, provide a more accurate evaluation of protocols when performing network simulations. This paper therefore analyzes the performance of a number of propagation models in a real indoor environment. Specifically, the Unit Disk, Lognormal Shadowing, Volcano Indoor Multi-Wall and WINNER II Stochastic channel models are investigated. Field measurements are performed in an office building to empirically determine the channel parameters and evaluate the models based on various error metrics. A network connectivity analysis is also performed using Monte Carlo simulations to demonstrate the impact the choice of the physical layer model has on the network backbone construction. This paper shows that the Volcano Indoor Multi-Wall and WINNER II Stochastic channel models provide better estimate of the actual path losses in an indoor environment. It also shows that the errors introduced can cause connectivity algorithms to significantly under-estimate (sometimes up to a 14 times under-estimation) the power requirements necessary to guarantee a connected network.