This chapter overviews long-standing foundations, methods, and concepts of dendrochronology, yet also pays attention to a few related paradigm shifts driven by isotope measurements in tree-rings. The basics of annual ring formation are first reviewed, followed by structural descriptions of tree-rings at the macroscopic-to-microscopic scale including earlywood and latewood in conifers (gymnosperms) and hardwoods (angiosperms), as well as wood anatomical features. Numerous examples of inter-disciplinary applications connected to various tree-ring parameters are provided. With the foundation of tree-rings established, this chapter then describes the process and necessity for crossdating—the process by which each and every ring is assigned to a specific year. Methods and terminology related to field sampling also briefly described. The long-standing paradigm of site selection criteria—well shown to maximize common signals in tree-ring width datasets—is challenged in a brief discussion of newer tree-ring isotope literature demonstrating that robust chronologies with high signal-to-noise ratios can be obtained at non-ecotonal locations. Opportunities for isotope measurements to enable crossdating in otherwise challenging contexts are likewise highlighted. The chapter reviews a conceptual framework to disaggregate tree-ring time-series, with special attention to detrending and standardization methods used to mitigate tree-age/size related noise common to many applications such as dendroclimatic reconstruction. Some of the drivers of long-term trends in tree-ring isotope data such as the increase in the atmospheric concentration of CO₂, age/size/height trends, and climate variation are presented along with related debates/uncertainties evident in literature in order to establish priorities for future investigations. The development of tree-ring chronologies and related quality control metrics used to assess the common signal and the variance of tree-ring data are described, along with the limitations in correlation based statistics to determine the robustness of tree-ring datasets particularly in the low frequency domain. These statistical methods will gain relevance as tree-ring isotope datasets increasingly approach sample replications and dataset structures typical for tree-ring width measurements.