The sensitivity of networks to the removal of vertices has been studied extensively over the last 15 years. A common approach to measuring this sensitivity is (i) successively removing vertices following a specific removal strategy and (ii) comparing the original and the modified network using a specific comparison method. In this paper we apply a wide range of removal strategies and comparison methods in order to study the sensitivity of medium-sized networks from the real world and randomly generated networks. In the first part of our study we observe that social networks and web graphs differ in sensitivity. When removing vertices, social networks are robust, web graphs are not. This effect is consistent with the work of Boldi et al. who analyzed very large social networks and web graphs. For randomly generated networks we find that their sensitivity depends significantly on the comparison method. The choice of removal strategy has surprisingly marginal impact on the sensitivity for removal strategies derived from common centrality measures. However, the removal strategy has a strong impact when removing the vertices in random order.