An event-triggered output feedback control approach is proposed via a disturbance observer and adaptive dynamic programming (ADP). The solution starts by constructing a nonlinear disturbance observer, which only depends on the measurement of system output. A state observer is then developed based on approximation information of system dynamics via neural networks. In order to avoid continuous transmission and reduce the communication burden in the closed-loop system, an event-triggered mechanism is introduced such that the control signal is updated only at a specific instant when a triggered condition is violated. By virtue of the disturbance observer and state observer, an output-feedback ADP control approach then is developed, where only a critic network is employed to estimate the value function. Based on the Lyapunov stability theory, the stability of the closed-loop system is rigorously analyzed, and the effectiveness of the proposed control approach is verified by two simulation examples.