Background: Surveillance of healthcare-associated infections is essential to prevention. A new collaborative project, namely ALADIN, was launched in January 2009 and aims to develop an automated detection tool based on natural language processing of medical documents. Objective: The objective of this study was to evaluate the annotation of natural language medical reports of healthcare-associated infections. Methods: A software MS Access application (NosIndex) has been developed to interface ECMT XML answer and manual annotation work. ECMT performances were evaluated by an infection control practitioner (ICP). Precision was evaluated for the 2 modules and recall only for the default module. Exclusion rate was defined as ratio between medical terms not found by ECMT and total number of terms evaluated. Results: The medical discharge summaries were randomly selected in 4 medical wards. From the 247 medical terms evaluated, ECMT proposed 428 and 3,721 codes, respectively for the default and expansion modules. The precision was higher with the default module (P₁=0.62) than with the expansion (P₂=0.47). Conclusion: Performances of ECMT as support tool for the medical annotation were satisfactory.