A state monitoring method comprising: a first step of obtaining a plurality of pieces of first measurement data of a first time length at timings different from one another from a sensor provided in a tested target while the tested target is normal; a second step of obtaining a plurality of pieces of second measurement data of the first time length at timings different from one another from the sensor when the tested target is diagnosed; a third step of randomly selecting a plurality of pieces of training data from the plurality of pieces of first measurement data; a fourth step of randomly selecting a plurality of pieces of test data from the plurality of pieces of second measurement data; a fifth step of dividing each of the plurality of pieces of training data into pieces of first segment data of a second time length shorter than the first time length and preparing a first feature value vector including a plurality of feature values for each divided piece of first segment data; a sixth step of preparing a classification boundary between normal and anomaly and an anomaly discriminant threshold value from a plurality of first feature value vectors prepared for each piece of first segment data for the plurality of pieces of training data; a seventh step of dividing each of the plurality of pieces of test data into pieces of second segment data of the second time length and preparing a second feature value vector including a plurality of feature values for each divided piece of second segment data; an eighth step of calculating an anomaly score representing a distance from the classification boundary for each second feature value vector and calculating, for each of the plurality of pieces of test data, an anomaly ratio representing a ratio of the number of anomaly scores of the second feature value vectors exceeding the anomaly discriminant threshold value to a total number of the second feature value vectors; and a ninth step of repeating the third to eighth steps a plurality of times and discriminating the tested target as being abnormal when an average value of obtained anomaly ratios exceeds a prescribed value. a first step of obtaining a plurality of pieces of first measurement data of a first time length at timings different from one another from a sensor provided in a tested target while the tested target is normal; a second step of obtaining a plurality of pieces of second measurement data of the first time length at timings different from one another from the sensor when the tested target is diagnosed; a third step of randomly selecting a plurality of pieces of training data from the plurality of pieces of first measurement data; a fourth step of randomly selecting a plurality of pieces of test data from the plurality of pieces of second measurement data; a fifth step of dividing each of the plurality of pieces of training data into pieces of first segment data of a second time length shorter than the first time length and preparing a first feature value vector including a plurality of feature values for each divided piece of first segment data; a sixth step of preparing a classification boundary between normal and anomaly and an anomaly discriminant threshold value from a plurality of first feature value vectors prepared for each piece of first segment data for the plurality of pieces of training data; a seventh step of dividing each of the plurality of pieces of test data into pieces of second segment data of the second time length and preparing a second feature value vector including a plurality of feature values for each divided piece of second segment data; an eighth step of calculating an anomaly score representing a distance from the classification boundary for each second feature value vector and calculating, for each of the plurality of pieces of test data, an anomaly ratio representing a ratio of the number of anomaly scores of the second feature value vectors exceeding the anomaly discriminant threshold value to a total number of the second feature value vectors; and a ninth step of repeating the third to eighth steps a plurality of times and discriminating the tested target as being abnormal when an average value of obtained anomaly ratios exceeds a prescribed value.