Information extraction tasks are particularly challenging in specific contexts such as the legal domain. In this paper, Named Entity Recognition is used to make legal texts more accessible to domain experts and laymen. This paper focuses on extracting law references and citations of court decisions, which occur in various syntactic formats. To investigate this task a reference data set is constructed from a large collection of German court decisions and different NER-techniques are compared. Pattern matching, probabilistic sequence labeling (CRF), Deep Learning (BiLSTM) and transfer learning using a pretrained language model (BERT) are applied to extract references to laws and court decisions. The results show that the BERT based approach achieves F1 scores around 0.98 for both tasks and outperforms methods from prior work, which achieve F1 scores of 0.89 (CRF for law references) respectively 0.82 (CRF for court decisions) on the same data set.