An algorithm, RENO (resynthesis for network optimization), for the optimization of multilevel combinational networks is presented. In RENO, a given network is minimized for area by optimally resynthesizing each gate, using other existing gates in the network. The resynthesis process is based on a covering-set algorithm, which enables one to resynthesize using complex gates instead of only simple gates (e.g., NAND and NOR), thereby exploring more reconfiguration possibilities. Due to the reconfiguration ability of the RENO algorithm, networks optimized by RENO have good quality, even if no network don't-care is used. The RENO algorithm has been implemented in both cube and shared-OBDD data structures. Experimental results obtained by RENO for benchmark functions and comparison with the optimization algorithm used in MIS 2.2 show that RENO is effective for multilevel network optimization.