The extraction of codes from Electronic Health Records (EHR) data is an important task because extracted codes can be used for different purposes such as billing and reimbursement, quality control, epidemiological studies, and cohort identification for clinical trials. The codes are based on standardized vo-cabularies. Diagnostics, for example, are frequently coded using the Interna-tional Classification of Diseases (ICD), which is a taxonomy of diagnosis codes organized in a hierarchical structure. Extracting codes from free-text medical notes in EHR such as the discharge summary requires the review of patient data searching for information that can be coded in a standardized manner. The manual human coding assignment is a complex and time-consuming process. The use of machine learning and natural language processing approaches have been receiving an increasing attention to automate the process of ICD coding. In this article, we investigate the use of Support Vector Machines (SVM) and the binary relevance method for multi-label classification in the task of auto-matic ICD coding from free-text discharge summaries. In particular, we ex-plored the role of SVM parameters optimization and class weighting for addressing imbalanced class. Experiments conducted with the Medical Infor-mation Mart for Intensive Care III (MIMIC III) database reached 49.86% of f1-macro for the 100 most frequent diagnostics. Our findings indicated that opti-mization of SVM parameters and the use of class weighting can improve the ef-fectiveness of the classifier.