In this paper, a method is presented which allows abnormal or unexpected operating conditions to be identified from measured response data. Potential applications of such a technique cover a wide range of engineering situations where a definite, early warning of an abnormal state is essential, but where classification of the particular abnormality is of lesser importance. In the technique described, unexpected operating conditions are identified by the presence of measured data which are significantly different from those known to correspond to normal operating conditions or responses. The proposed approach to the detection of such novel data is based upon probability density function (PDF) estimation using a kernel method. The theory behind this approach is presented for the multivariate case, and the need for data pre-processing in practical applications of PDF estimation is highlighted. The kernel method is demonstrated on simulated data sets. Finally, vibration response data from an electric motor with three levels of phase imbalance are used to illustrate the application of the PDF estimation method to the detection of abnormal conditions. The proposed method yields a clear indication of the existence of a fault in the AC motor, with no prior knowledge of the particular fault state.