The utilization of vision information for control in intelligent technical applications with high dynamics has been a central issue. The main obstacles are the low sampling rate of the cameras (about 60Hz of a commercial camera) compared to the required sampling rate of the high dynamic applications (over 1kHz) and the time-delayed measurements due to the image processing. A model-based Event-Triggered Observer (ETO)  has been proposed for linear systems to observe and predict the undelayed continuous state variables of the system from the sampled and delayed measurements where the sampling rate and the delay- time can be constant or variable. However, the disturbances like the sensor noise and the perturbations of the environment were not handled within . As is well known, the disturbances decline the performance of the model-based observer. To keep the performance of the state-estimation and prediction, an effective solution is to adopt into account the unknown dynamic effects due to the disturbances by integrating them into the state-estimation. A survey  has shown different methods of dealing with the disturbance estimation to improve the accuracy of the estimation of the system internal states. Unlike other mentioned methods in , the so-called Unknown Input Observer (UIO), shows significant advantages with the ability to estimate the unknown input disturbances and the system internal states simultaneously by augmenting the state space of a classical state observer like Luenberger-Observer with a disturbance model [14,15]. In this paper, an Extended Event-Triggered Observer (EETO) is proposed, which based on the proposed ETO in  and is extended with an UIO. The new proposed EETO can estimate undelayed continuous state variables from the time-delayed measurements under the disturbances. An example system with a second-order delay behavior is simulated, whose output is sampled and delayed. The simulation results show the performance of the proposed Extended Event-Triggered Observer.