The Maximum Likelihood (ML) estimation method has been extensively applied to identifying the parameters of an aircraft, but it has to derive sensitivity equations in advance and solve sensitivity matrices, thus being inconvenient for its application and easily reaching locally optimal solutions. The paper proposes an aircraft"s parameter identification algorithm by combining the cloud model optimization with the ML estimation method. The algorithm proposed in the paper satisfactorily overcomes the shortcomings of the ML estimation method. Its detailed procedural steps are presented. The numerical results show that the parameter identification algorithm is easy to implement, has good identification precision and fast convergence and does not reach locally optimal solutions.