Structural health inspection has been widely applied in the operation of wind farms to find early cracks in wind turbine blades (WTBs). Increased numbers of turbines and expanded rotor diameters are driving up the workloads and safety risks for site employees. Therefore, it is important to automate the inspection process as well as minimize the uncertainties involved in routine blade health inspection. In addition, crack documentation and trending is vital to assess rotor blade and turbine reliability in the 20 year designed life span. A new crack recognition and classification algorithm is described that can support automated structural health inspection of the surface of large composite WTBs. The first part of the study investigated the feasibility of digital image processing in WTB health inspection and defined the capability of numerically detecting cracks as small as hairline thickness. The second part of the study identified and analyzed the uncertainty of the digital image processing method. A self-learning algorithm was proposed to recognize and classify cracks without comparing a blade image to a library of crack images. The last part of the research quantified the uncertainty in the field conditions and the image processing methods.