Manually analysing logfiles is a very time consuming and error-prone effort. By developing a system to automatically analysing the logfiles it is possible to both increase the speed and accuracy of the analysis. This thesis presents a method for automatic anomaly detection in logfiles using statistical analysis and threshold based classification. The presented method uses five different threshold based approaches to identify anomalous entries within a logfile. Each of the five approaches was successful in identifying and reporting perceived anomalies within 805 logfiles provided by Sandvine, it was however not possible to do a formal evaluation of the results due to a lack of a ground truth.