Steam sterilizers are an essential part of a dental office. They ensure that the instruments utilized in the mouth, teeth and jaw of the patient are sterile. It is crucial for the dentist that the downtimes of these devices are as small as possible or completely avoided as a failure of the latter can lead to a disruption of the complete workflow or in the worst case to a treatment stop in his practice. Moreover, as a service provider, W&H Dentalwerk aims at keeping the emerging maintenance costs as small as possible. Thus, to satisfy the customer and reduce our service costs, they are putting a great effort in researching intelligent procedures for the early detection and classification of failures in our product, the W&H Lisa sterilizer. In this talk, Alexander will present the current results and experiences on their ongoing journey to establish a framework for Predictive Maintenance in our company. In this context, he will discuss the difficulties with regard to data collection and preparation, outline the Machine Learning based model development process and present results regarding the prediction quality of the anomaly detection models based on a significant amount of sterilizer data coming from dental offices. Currently, they are able to detect anomalies in sterilizers with an accuracy of 75%.