Although the current literature has recorded many reports of identifying components from herbal preparations, all of them were largely limited to target components. This paper provides a novel and generally applicable approach to identifying nontarget components from herbal preparations, based on the use of liquid chromatography ion trap time-of-flight mass spectrometry (LC/MS-IT-TOF). A simple program was originally developed for searching the common diagnostic ions from all experimentally generated ions. The components sharing the exact same ions (mass error < 5 mDa) were classified into a family. All families were then connected into a coherent network by the bridging components that are present in two or more families. With the benefit from such a network, it is feasible to sequentially characterize the structures of all diagnostic ions once a single component has been de novo identified. The structures of the diagnostic ions could then be used as "a priori" information for selecting the exact candidates containing the substructures of the corresponding diagnostic ions from the primary database hits. This strategy enables a nearly 7-fold narrowing of the database hits and thus substantially enhances the analytical efficiency and sharpness. With the use of such an approach, 43 out of 53 components incorporated into the network have been successfully identified from the test herbal preparation. For the rest, components failed to be identified using this approach; a complementary approach to screening by sequential loss of specific chemical groups, proposed from the accurate mass differences between fragments, was established to narrow the database hits. All of the 87 peaks detected have been successfully identified by combining the use of both approaches except failed to differentiate some isomers. The presently developed approach and methodology would be useful for the identifications of complicated nontarget components from various complex mixtures such as herbal preparations, biological, and environmental samples.