Dynamic engineering of application-specific network traffic is becoming more important for applications that consume large amounts of network resources, in particular, bandwidth. Since traditional traffic engineering approaches are static they cannot address this trend; hence there is a need for real-time traffic classification to enable dynamic traffic engineering. A packet flow monitor has been developed that operates at full Gigabit Ethernet line rate, reassembling all TCP flows in real-time. The monitor can be used to classify and analyse both plain text and encrypted application traffic. This dissertation shows, under reasonable assumptions, 100% accuracy for the detection of bulk data traffic for applications when control traffic is clear text and also 100% accuracy for encrypted GridFTP file transfers when data channels are authenticated. For non-authenticated GridFTP data channels, 100% accuracy is also achieved, provided the transferred files are tens of megabytes or more in size. The monitor is able to identify bulk flows resulting from clear text control protocols before they begin. Bulk flows resulting from encrypted GridFTP control sessions are identified before the onset of bulk data (with data channel authentication) or within two seconds (without data channel authentication). Finally, the system is able to deliver an event to a local publish/subscribe server within 1 ms of identification within the monitor. Therefore, the event delivery introduces negligible delay in the ability of the network management system to react to the event.