The increased use of systems based on Electronic Health Records caused an enormous increment of information available electronically, which can be processed by Data Mining techniques, leading to relevant findings. The expected result was that this information becomes easy to access, analyze and share. However, the text present in the clinical notes is written in natural language, and is, thus, unstructured, and difficult to automatically process. These clinical notes might contain pertinent data for the health of the patient. In this thesis, with the help of Natural Language Processing and Information Extraction techniques, we present a system that, given a clinical note, extracts relevant named entities from it, such as names of diseases, symptoms, treatments, diagnosis and drugs, generating structured information from unstructured free text. In addition, in order to avoid privacy issues and considering that these clinical notes might contain references to names of patients, doctors or another health professionals, we also present an anonymization step. Finally, we add a module that automatically corrects typos from these medical notes. Final results show that the system, in general, is able to recognize and interpret medical entities.