Generalized hill climbing (GHC) algorithms provide a well-defined framework for describing the performance of local search algorithms for discrete optimization problems. Necessary and sufficient convergence conditions for GHC algorithms are presented. These convergence conditions are derived using a new iteration classification scheme for GHC algorithms. The implications of the necessary and the sufficient convergence conditions for GHC algorithms with respect to existing convergence theory for simulated annealing are also discussed.