We develop a comprehensive framework for analyzing full record statistics, covering record counts M (t1), M (t2), . . ., and their corresponding attainment times T M (t 1 ) , T M (t 2 ) , . . ., as well as the intervals until the next record. From this multiple-time distribution, we derive general expressions for various observables related to record dynamics, including the conditional number of records given the number observed at a previous time and the conditional time required to reach the current record, given the occurrence time of the previous one. Our formalism is exemplified by a variety of stochastic processes, including biased nearest-neighbor random walks, asymmetric run-and-tumble dynamics, and random walks with stochastic resetting.