Monitoring vegetation restoration is challenging because monitoring is costly, requires long-term funding, and involves monitoring multiple vegetation variables which are often not linked back to learning about progress toward objectives. There is a clear need for the development of targeted monitoring programs that focus on a reduced set of variables that are tied to specific restoration objectives. In this paper, we present a method to progress the development of a targeted monitoring program, using a pre-existing state-and-transition model. We i) use field data to validate an expert-derived classification of woodland vegetation states; ii) use this data to identify which variable(s) help differentiate woodland states; and iii) identify the target threshold (for the variable) that signifies the desired transition has been achieved. The measured vegetation variables from each site in this study were good predictors of the different states. We show that by measuring only a few of these variables, it is possible to assign the vegetation state for a collection of sites, and monitor if and when a transition to another state has occurred. For this ecosystem and STM, out of nine vegetation variables considered, the density of immature trees and percentage of exotic understorey vegetation cover were the variables most frequently specified as effective to define a threshold or transition. We synthesise findings by presenting a decision tree that provides practical guidance for the development of targeted monitoring strategies for woodland vegetation.