The structural characteristics of a healthy tendon are related to the anisotropic speckle patterns observed in ultrasonic images. This speckle orientation is disrupted upon damage to the tendon structure as observed in patients with tendinopathy. Quantification of the structural appearance of tendon shows promise in creating a tool for diagnosing, prognosing, or measuring changes in tendon organization over time. The current work describes a first step taken towards this goal-classification of Achilles tendon images into tendinopathy and control categories. Eight spatial frequency parameters were extracted from regions of interest on tendon images, filtered and classified using linear discriminant analysis. Resulting algorithms had better than 80% accuracy in categorizing tendon image kernels as tendinopathy or control. Tendon images categorized wrongly provided for an interesting clinical association between incorrect classification of tendinopathy kernels as control and the symptom and clinical time history based inclusion criteria. To assess intersession reliability of image acquisition, the first 10 subjects were imaged twice during separate sessions. Test-retest of repeated measures was excellent (tau = 0.996, ICC = (2, 1) = 0.73 with one outlier) indicating a general consistency in imaging techniques.