Fatigue crack growth during the service life of aging aircraft is a critical issue and monitoring of such cracks in structural hotspots is the goal of this research. This paper presents a procedure for classification and detection of cracks generated in bolted joints which are used at numerous locations in aircraft structures. Single lap bolted joints were equipped with surface mounted piezoelectric (pzt) sensors and actuators and were subjected to cyclic loading. Crack length measurements and sensor data were collected at different number of cycles and with different torque levels. A classification algorithm based on Support Vector Machines (SVMs) was used to compare signals from a healthy and damaged joint to classify fatigue damage at the bolts. The algorithm was also used to classify the amount of torque in the bolt of interest and determine if the level of torque affected the quantification and localization of the crack emanating from the bolt hole. The results show that it is easier to detect the completely loose bolt but certain changes in torque, combined with damage, can produce some non-unique classifier solutions.