Automated anomaly detection in sensor data plays a crucial role in various applications, including predictive maintenance, quality control, and prototype testing in the automotive industry. This paper focuses on a specific type of anomaly, known as “Spikes”, which are sharp, sudden outlier values with no correlation to surrounding samples. The study presents a novel Dynamic Time Warping (DTW) based technique for detecting these spikes in online, multi-channel acquisitions during automotive testing. The technique has been validated on a real- world dataset acquired during a measurement campaign on an electric vehicle. The dataset consists of both anomalous and non-anomalous signals with varying dynamic ranges, patterns, lengths, and sensor types. The results show the method’s accuracy in avoiding false positives, such as mistaking spikes for other physical impulses during the test, like tires squeaking, or any other physical impulse coming from the engine or other sub-component of the vehicle under test.