A mobile network provides a continuous stream of data describing the performance of its cells. Most of the data describes cells with acceptable performance. Detecting and analysing mobile network cells with quality problems from the data stream is a tedious and continuous problem for network operators. Anomaly detection can be used to identify cells, whose performance deviates from the average and which are potentially having some sub-optimal configuration or are in some error condition. In this paper we provide two methods to detect such anomalously behaving cells. The first method estimates the distance from a cell to an optimal state and the second one is based on detecting the support of the data distribution using One-Class Support Vector Machine (OC-SVM). We use the methods to analyse a data sample from a live 3G network and compare the analysis results. We also show how clustering of found anomalies can be used to find similarly behaving cells that can benefit from the same corrective measures.