Anomaly detection (a.k.a. outlier detection) is the identification of events that do not conform to an expected pattern in a dataset. When applied to monitoring modern, complex IT systems, it keeps track of a plethora of incoming data streams. This paper provides an approach that uses the lowest and most unstructured source of data related to an IT system - the raw system log files. Several versions and parametrizations of basic building blocks will be presented to show how different types of anomalies can be extracted from the data. Several experiments on synthetic as well as real-world data show effectiveness of the algorithm. Special care is taken to keep the model and the resulting alerts interpretable - since detecting an error without a meaningful explanation about its details is of limited use to end user (the results need to be actionable).