Summary: A successive quadratic programming algorithm for solving a nonlinear program is introduced. Its speciality is that in each iteration of the algorithm the constraints of the quadratic program are not only the linear approximations of the original constraints, but have a perturbation vector also. Under certain conditions its locally R-linear rate of convergence is proved. In the final part of the paper conditions for superlinear convergence is discussed.