Within the general purpose of information extraction, detection of event descriptions is an important clue. A word refering to an event is more powerful than a single word, because it implies a location, a time, protagonists (persons, organizations\dots). However, if verbal designations of events are well studied and easier to detect than nominal ones, nominal designations do not claim as much definition effort and resources. In this work, we focus on nominals desribing events. As our application domain is information extraction, we follow a named entity approach to describe and annotate events. In this paper, we present a typology and annotation guidelines for event nominals annotation. We applied them to French newswire articles and produced an annotated corpus. We present observations about the designations used in our manually annotated corpus and the behavior of their triggers. We provide statistics concerning word ambiguity and context of use of event nominals, as well as machine learning experiments showing the difficulty of using lexicons for extracting events.