It is increasingly acknowledged that emotions emerge from connectivity between distributed brain networks (Engen et al., 2017; Lindquist & Barrett, 2012; Pessoa, 2017). In the present study, we used an unsupervised classification algorithm to reveal both consistency and degeneracy in neural network connectivity during anger and anxiety. Degeneracy refers to the ability of different biological pathways to produce the same outcomes (Edelman & Gally, 2001; Tononi et al., 1999). Twenty-four subjects underwent fMRI while listening to unpleasant music and self-generating experiences of anger and anxiety. A data-driven model building algorithm with unsupervised classification (S-GIMME; Gates et al., 2017) identified consistent patterns of between-network connectivity that characterized anger vs. anxiety as well as degenerate network patterns within each category. These findings are consistent with the constructionist account that emotion categories are populations of variable instances.