In this paper, we extend the switching BFGS/DFP algorithm of Fletcher and the switching BFGS/SR1 algorithm of Al-Baali to a class of switching type algorithms proposed within the Broyden family of quasi-Newton methods for unconstrained optimization. We propose some members of this class, which switch among the BFGS, the SRI and other desirable methods from the preconvex class. The switching technique is made in a certain sense upon the basis of estimating the size of the eigenvalues of the Hessian approximation. The results on a set of standard test problems show that several switching methods improve over the BFGS method and work almost similarly to certain idealized methods.