Anomaly Detection in Argon Oxygen Decarburization (AOD) Processes is crucial for optimal functioning and for uninterrupted production in steel industry. In this prestudy, we aim to detect an anomaly called calibration error on a flow sensor in AOD converter. To this purpose, we acquire data from Alleima, a steel company in Sweden. This data consists of signals from five different sensors including two flow sensors for measuring oxygen and argon flows, control signals for the corresponding valves and the pressure after the gases are mixed. This data is manually annotated as calibration error on a flow sensor-based anomaly (CEFSBA) and normal class by the experts. From each of the sensor signals, two features namely entropy and variance are extracted. These features are fed to classifier to classify a feature vector into normal and CEFSBA. Our proposed approach detected CEFSBA with an accuracy of 92.1% when using neural networks. Our prestudy, indicates that machine learning-based approaches can be used to identify CEFSBA in the AOD converter. However, in order to use in industry, the performance needs to be improved before deploying it for real time CEFSBA detection.