1. A method of identifying anomalous data in a test data set, the method comprising: storing one or more standard data sets of a plurality of standard data cases, each standard data case containing one or more standard data points representing normal, non-anomalous, data; calculating a first average distance measure between one or more test data points in the test data set and one or more of the nearest neighbouring corresponding standard data points in at least two of the plurality of standard data cases; determining a second average distance measure between the nearest neighbouring corresponding standard data points in at least two of the plurality of standard data cases; calculating a normalised distance measure relating to the one or more test data points in dependence on at least the first and second average distance measures; and identifying a test data point as anomalous in dependence on the value of the calculated normalised distance measure for the test data point.