In today's computer network systems lots of events are constantly written to log files. Unfortunately there is no common standard defining the structure of these event messages which are partly in human readable natural language form. This lack of structure makes automatic processing a lot more difficult. This master thesis describes the architecture and implementation of the LoP-System, a system that attempts to create machine readable event structures from ordinary log file events by natural language processing. The thesis explains implementational details as well as the theoretical concepts used. The core of the system consists of a series of cascaded but independent components, partly enhanced with machine learning techniques. The raw input is first processed by a simple recursive descent parser which recognizes syntactical features (e.g. IP addresses) and is then passed on to a part-of-speech tagger based on a hidden Markov model. Applying regular expression patterns to the tagged words is used to combine them to basic word groups (e.g. noun groups), which are subsequently semantically analyzed. The final step is the construction of the output events by a rule based event constructor. All components are implemented in Haskell, a purely functional programming language. Some of the components developed during this thesis, especially the part-of-speech tagger, are general natural language processing tools and can be applied to other domains.