SanskritTagger is a stochastic tagger for unpreprocessed Sanskrit text. The tagger tokenises text with a Markov model and performs part-of-speech tagging with a Hidden Markov model. Parameters for these processes are estimated from a manually annotated corpus of currently about 1.500.000 words. The article sketches the tagging process, reports the results of tagging a few short passages of Sanskrit text and describes further improvements of the program. The article describes design and function of SanskritTagger, a tokeniser and part-of-speech (POS) tagger, which analyses ”natural”, i.e. unannotated Sanskrit text by repeated application of stochastic models. This tagger has been developped during the last few years as part of a larger project for digitalisation of Sanskrit texts (cmp. (Hellwig, 2002)) and is still in the state of steady improvement. The article is organised as follows: Section 1 gives a short overview about linguistic problems found in Sanskrit texts which influenced the design of the tagger. Section 2 describes the actual implementation of the tagger. In section 3, the performance of the tagger is evaluated on short passages of text from different thematic areas. In addition, this section describes possible improvements in future versions.