Data collected in many applications have a form of sequences of events. One of the popular data mining problems is discovery of frequently occurring episodes in such sequences. Efficient algorithms discovering all frequent episodes have been proposed for sequences of simple events associated with basic event types. But in many cases events are described by a set of attributes rather than by just one event type attribute. The solutions handling such complex events proposed so far assume that a user provides a template of episodes to be discovered. This assumption does not allow users to discover all surprising relationships between event attributes. In this paper, we propose extensions to algorithms initially designed for simple events making them capable of handling complex events in the same manner.