In the case of quadrotors, system identification is a challenging task because quadrotors are inherently unstable exhibit nonlinear behavior and significant coupling. In addition to this, quadrotors’ behavior is greatly influenced by characteristics and coefficients, which are very hard to measure directly or determine analytically. However, all the difficulties listed above are known to be successfully overcome by the use of artificial intelligence. In this paper, two system identification techniques were applied and compared to model quadrotor attitude dynamics. These techniques are Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and continuous-time transfer function. © 2021 Taylor & Francis.