Understanding the patterns of past disturbance allows further insight into the complex composition, structure, and function of current and future forests, which is increasingly important in a world where disturbance characteristics are changing. Our objectives were to define disturbance causes, rates (percent disturbance per decade), magnitudes and frequency (time since last disturbance) for both secondary and old-growth mixed-oak stands, and to determine if all mixed oak stands experience similar disturbance history. The study was located in two southern Appalachian forests in western North Carolina, USA: Coweeta Hydrologic Laboratory, a 2,185 ha experimental forest with some history of harvesting, and the Joyce Kilmer Wilderness, a 6,805 ha old-growth forest with no known harvesting. We used dendroecological techniques to evaluate the disturbance histories and create chronologies of these mixed-oak forests. Average decadal disturbance rates ranged from 4.3% to 13.8%, similar to rates common in eastern temperate forests (5% to 20%). The decades of peak recruitment common to several stands were the 1840s, which coincides with the historical accounts of a hurricane; the 1900s through the 1940s, which coincide with logging and elimination of Castanea dentata (Marshall) Borkh. by chestnut blight; and the 1960s, which coincides with drought and an elm spanworm infestation. The large peaks of disturbance were often synchronous and widespread, affecting stands across both Coweeta and Joyce Kilmer. However, there were also scattered pulses of disturbance unique to single stands, suggesting that localized events also played a role in the disturbance dynamics. Periods of constant low rates of disturbance present in all stands also indicate the importance of small canopy gaps in these forests. We found that stands similar in disturbance regimes were also similar in species composition. Results from our study provide information on how past disturbances, both regional and local events, have shaped the current forest. This understanding could help inform models to better predict how forests might respond to future climate (e.g., rising temperatures and increasing precipitation variability) and disturbance patterns (e.g., more frequent and severe events).