Temporal lobe epilepsy (TLE) is characterized by debilitating and progressive cognitive impairment, but there is significant variability in the nature and severity of impairment across patients. Cognitive phenotyping is a promising approach for understanding the heterogeneity within TLE. This 3-paper dissertation aimed to identify the neural correlates associated with cognitive phenotypes, investigate methods for defining phenotypes, and examine epilepsy and health-related factors associated with each phenotype. Study 1 (Reyes et al., 2019) identified four distinct cognitive phenotypes in TLE (N = 70; 36.14 average age; 13.34 average education; 52% female) based on neuropsychological measures of memory and language. Each phenotype was associated with a unique pattern of white matter (WM) abnormalities. Patients with generalized impairment demonstrated widespread WM alterations, those with domain-specific impairment demonstrated regional WM alterations, and those with no impairment demonstrated WM patterns similar to controls. Study 2 (N = 407; 36.36 average age; 13.22 average education; 55% female; Reyes et al., 2020) compared phenotype classifications based on a neuropsychological approach (clinically-driven) versus cluster analysis (data-driven). Both approaches identified three unique cognitive phenotypes with strong agreement (kappa=.716); however, cluster analysis misclassified 12% of impaired patients as having normal cognition. These findings led to the question: could a more robust, person-centered, data-driven approach improve phenotyping? Study 3 (N = 1,178; 37.76 average age; 13.94 average education; 57% female; Reyes et al., under review) used latent profile analysis (LPA) to test several models of cognitive phenotyping and adjudicate the impact of missing data to identify the “best” taxonomy. LPA revealed that the three-class model was the optimal solution (entropy=.816) and the most robust to missing data with a 98.98% agreement with an imputed dataset (kappa=.983). Preliminary analyses revealed lower subcortical volumes in patients with generalized impairment and higher intracranial volumes in those with an intact profile. There was a differential association between hyperlipidemia and cognitive performance across phenotypes. These studies demonstrate unique cognitive phenotypes exist within TLE that are stable across investigations and approaches and are characterized by distinct neural signatures. Knowledge of these phenotypes could drive cognitive and neuroanatomical taxonomies in epilepsy and enhance individualized prediction of cognitive trajectories.