Folding transition states and their structures are crucial in understanding protein folding pathways and folding dynamics. As they cannot be detected directly by experiments due to their instability, many computational methods have been proposed to solve this problem. However, each of these methods can give only one part of the transition state ensemble for a peptide or protein. Here we present a folding-network approach to identify the transition states of peptides or proteins and test it on the β-hairpin peptide trpzip2, with the result that we identify all the folding transition states of tripzip2, which may only be determined separately by other methods. This suggests that the network approach can provide more complete information about the folding transition states, at least for peptides.