The state observer is being used widely because it has the advantage of the guarantee of reliability on financial problem, over heating, and physical shock. However, an Luenberger observer and a Sliding observer have such problems that an experimenter needs to know dynamics and parameters of the system. And also, the high gain observer has such a problem that it has transient state at the beginning of the observation. In this paper, the Neuro observer is proposed to improve these problems. The proposed Neuro observer complement a problem that occur from increase of gain of High-gain observer in proportion to the square number of observable state variables. And also, the proposed Neuro observer can tune the gain obtained by differentiating observational error at transient state automatically by using the backpropagation training method to stabilize the observational speed. To prove a performance of the proposed observer, it is simulated that the comparison between the state estimate performance of the proposed observer and that of Sliding, High gain observer is made by using a sinusoidal input to the observer which consists of four layers in stable 2nd order system.