Many real world problems can be modeled as nonlinear optimization problems where the first order derivatives of the objective function and constraints can be neither calculated nor approximated explicitly. This has motivated an a great interest and increasing interest toward the definition and the study of globally convergent derivative-free methods. These methods produce sequences of points "converging" towards stationary points of the considered minimization problem by employing only the information deriving from the function values. This talk considers a particular class of derivative free methods which have the common feature of enforce the global convergence by using line search techniques along suitable search directions. First the main ideas and results of these methods are briefly recalled. Then some algorithms for particular class of nonlinear optimization problems are described.