Understanding the neural mechanisms underlying serially ordered behavior is a fundamental problem in motor learning. We present a computational model of sensorimotor learning in songbirds that is constrained by the known functional anatomy of the song circuit. The model subsumes our companion model for learning individual song "syllables" and relies on the same underlying assumptions. The extended model addresses the problem of learning to produce syllables in the correct sequence. Central to our approach is the hypothesis that the Anterior Forebrain Pathway (AFP) produces signals related to the comparison of the bird's own vocalizations and a previously memorized "template." This "AFP comparison hypothesis" is challenged by the lack of a direct projection from the AFP to the song nucleus HVc, a candidate site for the generator of song sequence. We propose that sequence generation in HVc results from an associative chain of motor and sensory representations (motor --> sensory --> next motor. ) encoded within the two known populations of HVc projection neurons. The sensory link in the chain is provided, not by auditory feedback, but by a centrally generated efference copy that serves as an internal prediction of this feedback. The use of efference copy as a substitute for the sensory signal explains the ability of adult birds to produce normal song immediately after deafening. We also predict that the AFP guides sequence learning by biasing motor activity in nucleus RA, the premotor nucleus downstream of HVc. Associative learning then remaps the output of the HVc sequence generator. By altering the motor pathway in RA, the AFP alters the correspondence between HVc motor commands and the resulting sensory feedback and triggers renewed efference copy learning in HVc. Thus, auditory feedback-mediated efference copy learning provides an indirect pathway by which the AFP can influence sequence generation in HVc. The model makes predictions concerning the role played by specific neural populations during the sensorimotor phase of song learning and demonstrates how simple rules of associational plasticity can contribute to the learning of a complex behavior on multiple time scales.