An automatic method to extract term candidates from the medical field by applying linguistic techniques is presented. Semantic, morphological and syntactic rules were used to develop this term extractor. On the first phase, the detection was performed by applying a standard dictionary. This dictionary was uploaded to the analyzer software that assigned the tag 'TC' ('Term Candidates') to the words that could be considered terms. Morphological and syntactic rules were used to try to deduce the part of speech of the words that were not considered on the dictionary (WNCD). Afterwards, nominal phrases that included WNCD were gathered to extract them as term candidates of the field. Smorph, Post Smorph Module (MPS) - both work on groups- and Xerox's Xfst were the software used in this project. Smorph performs the morphological analysis of character strings, which yields morphological and POS tagging allocation for each occurrence according to the features given. MPS, in turn, uses the output of Smorph as its input and, from recomposition, decomposition and correspondence rules established by the user, analyzes the headword string that results from the morphological analysis. Xfst is a finite state tool that works on character strings assigning previously stated categories to allow, then, the automatic analysis of expressions. This method was tested on a section of the corpus of clinical cases collected by Burdiles (CCCM - 2009) of 217258 words. The results were evaluated according to precision and recall measures under expert guidance.