Treebanks are linguistically annotated corpora with some previously established scheme of grammatical analysis. In any domain and in the (bio-) medical field in particular, such resources constitute a fundamental piece of knowledge for empirically-based, data-driven language processing, human language technologies and linguistic research and have attracted an increased interest during recent years. The interest for treebanks in biomedicine is guided by the fact that information extraction and (bio-) text mining research is shifting focus from the extraction and annotation of named entities to the extraction and annotation of relations and interactions between entities. This is usually associated by the extraction of verbal – alias predicate-argument – structures (cf. Kulick et al. [1], Tateisi et al. [2]). Semantic relations (e.g. between entities) and role extraction and labelling (e.g. agent, object) constitute a considerable challenge for automatic tools, although recent evaluation competitions such as the PASBio (Wattarujeekrit et al., [3]) and the BioCreAtIvE (Hirschman et al., [4]) revealed that some systems could present significant progress in performance in this area. In this paper, we present our current activities towards the compilation and the multi-layered annotation of a domain-dependent corpus for Swedish in the area of medicine. The focus of the paper is based on the description of the constituent structure and functionally oriented annotation of the corpus. Moreover, the annotation scheme adopted, which incorporates three main layers of linguistic processing, lexical analysis, shallow semantic analysis and syntactic processing, will be exemplified. For the syntactic analysis we use a cascaded finite-state parser, aware of the shallow semantic annotations produced. The result of this analysis, including syntactic parsing and shallow semantic analysis, is transformed into the TIGER-XML interchange format ([5]). Our goal is to produce a large, rich in annotations, medical treebank suitable for both corpus-based grammar learning systems, for semantic relation extraction and for linguistic exploration of theoretical nature. Motivation for this work is given in Section 2. Background work in the area of biomedical syntactic analysis and treebanking is presented in Section 3. Section 4 gives a brief description of the corpus used in this work, while Section 5 deals with the pre-processing steps applied into a sample of the corpus. Section 6 presents evaluation results based on this sample, while Section 7 summarizes the paper and proposes directions for future work.