Monitoring the effect of ecosystem restoration can be difficult and time consuming. Autonomous sensors, such as acoustic recorders, can aid monitoring across long time scales. This project successfully developed, tested and implemented call recognisers for eight species of frog in the Murray-Darling Basin. Recognisers for all but one species performed well and substantially better than many species recognisers reported in the literature. We achieved this through a comprehensive development phase, which carefully considered and refined the representativeness of training data, as well as the construction (amplitude cut-off) and the similarity thresholds (score cut-offs) of each call template used. Recogniser performance was high for almost all species examined. Recognisers for C. signifera, L. fletcherii, L. dumerilii, L. peronii, and C. parinsignifera all performed well, with most templates having ROC values (the proportion of true positive and true negatives) over 0.7, and some much higher. Recognisers for L. peronii, L. fletcherii and L. dumerilii performed particularly well in the training dataset, which allowed for responses to environmental watering events, a restoration activity, to be clearly observed. While slightly more involved than building recognisers using commercial packages, the workflows ensure that a high quality recogniser can be built and the performance fine-tuned using multiple parameters. Using the same framework, recognisers can be improved in future iterations. We believe that multi-species recognisers are a highly effective and precise way to detect the effects of ecosystem restoration.