Conformational change in polymers including proteins is central to many molecular processes. Defining conformational states, however, remains a difficult and increasingly common problem, with many existing methods based on arbitrary or potentially unrepresentative measures. Furthermore, the expanding length of molecular dynamics simulations and direct observation of transitions between different energy basins suggest that this issue will only become evermore important. Methods commonly used to characterize conformational states include principal component analysis, root-mean-square deviation-based clustering, and geometric measurements such as hinge angles and distances. Here we present a method where the eigenvector frequencies derived from a Gaussian network model (Bahar, L; Atilgan, A. R.; Erman, B. Folding Des. 1997, 2, 173-181) of a trajectory of structures from a molecular dynamics simulation are used to describe the state of the protein at each time point. We apply the method to three proteins that share the same fold as the type 11 periplasmic binding proteins: The lysine-arginine-ornithine-binding protein, the glutamine-binding protein, and the ligand-binding domain from the NR1 N-methyl-D-aspartate receptor. We find that the method can distinguish different states in good agreement with a variety of previous analyses and additionally provides information on the dynamic properties of that system at a given time point.