Timely mortality surveillance in France is based on the monitoring of electronic death certificates to provide information to health authorities. This study aims at analyzing the performance of a rule-based and a supervised machine learning methods to classify medical causes of death into 60 mortality syndromic groups (MSGs). Performance was first measured on a test set. Then we compared the evolutions of the monthly numbers of deaths classified into MSGs from 2012 to 2016 using both methods. Among the 60 MSGs, 31 achieved recall and precision over 0.95 for either one or the other method on the test set. On the whole dataset, the correlation coefficient of the monthly numbers of deaths obtained by the two methods were close to 1 for 21 of 31 MSGs. This approach is useful to analyze a large number of categories or when the annotated resources are quite limited.