Method for monitoring turbines (122, 124) of a windmill farm, comprising the following steps: • providing a global nominal dataset containing frame data of the turbines (122, 124) of the windmill farm and continuous reference monitoring data of the turbines (122, 124) for a first period in the fault free state, wherein the reference monitoring data comprise at least two same monitoring variables for each turbine (122, 124), • building a nominal global model based on the global nominal dataset and relationships between the windmill turbines (122, 124) present in the windmill farm by statistical techniques during a fault-free time period, and clustering (22, 24, 26, 126, 128, 130) the turbines (122, 124) according thereto, • assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering (22, 24, 26, 126, 128, 130), • building a nominal local model for the turbines of each cluster (22, 24, 26, 126, 128, 130) based on the respective assigned nominal local datasets and based on multivariate statistical algorithms or artificial intelligence techniques, wherein the nominal local model is built in that way, that a nonconformity index is provideable which is indicating the degree of nonconformity between data and the model, • providing a test dataset with continuous test monitoring data of the turbines of the windmill farm for a further period, wherein those continuous test monitoring data are structured in the same way than the continuous reference monitoring data in the nominal global dataset and wherein the clustering (22, 24, 26, 126, 128, 130) of the nominal global dataset is also applied on the test dataset, • cluster wise (22, 24, 26, 126, 128, 130) projection of continuous test monitoring data of the test dataset on the respective assigned nominal local models of the turbines and deriving a nonconformity index for each respective turbine therefrom, • indicating a turbine (122, 124) as critical in case that the respective related nonconformity index exceeds a given limit. • providing a global nominal dataset containing frame data of the turbines (122, 124) of the windmill farm and continuous reference monitoring data of the turbines (122, 124) for a first period in the fault free state, wherein the reference monitoring data comprise at least two same monitoring variables for each turbine (122, 124), • building a nominal global model based on the global nominal dataset and relationships between the windmill turbines (122, 124) present in the windmill farm by statistical techniques during a fault-free time period, and clustering (22, 24, 26, 126, 128, 130) the turbines (122, 124) according thereto, • assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering (22, 24, 26, 126, 128, 130), • building a nominal local model for the turbines of each cluster (22, 24, 26, 126, 128, 130) based on the respective assigned nominal local datasets and based on multivariate statistical algorithms or artificial intelligence techniques, wherein the nominal local model is built in that way, that a nonconformity index is provideable which is indicating the degree of nonconformity between data and the model, • providing a test dataset with continuous test monitoring data of the turbines of the windmill farm for a further period, wherein those continuous test monitoring data are structured in the same way than the continuous reference monitoring data in the nominal global dataset and wherein the clustering (22, 24, 26, 126, 128, 130) of the nominal global dataset is also applied on the test dataset, • cluster wise (22, 24, 26, 126, 128, 130) projection of continuous test monitoring data of the test dataset on the respective assigned nominal local models of the turbines and deriving a nonconformity index for each respective turbine therefrom, • indicating a turbine (122, 124) as critical in case that the respective related nonconformity index exceeds a given limit.