Healthy tendon's structural characteristics are related to the anisotropic speckle pattern observed in ultrasound 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 monitoring changes in tendon organization over time. Previously we showed the feasibility of using spatial frequency parameters and Linear Discriminant Analysis (LDA) to categorize tendon tissue as normal or tendinopathic with better than 80% accuracy. The current work aimed to improve accuracy by developing an Artificial Neural Network (ANN) classifier and compare its results with those achieved in our previous LDA work. The eight spatial frequency parameters used in our previous work were extracted from regions of interest (ROI) on tendon images, filtered and classified using an ANN classifier. The spatial frequency parameters were used as inputs to the ANN. For a tendon tested with an ANN trained for that type of tendon, the accuracy was very high, with a correct classification rate (CCR) of 95-99%. However, when testing a tendon with an ANN trained for a different tendon type, the CCR was only 72-75%. This seems to indicate that a unique ANN needs to be trained for each type of tendon. The high CCRs obtained using the tendon-specific ANNs suggest that this novel discrimination strategy may lead to a robust tool for diagnosing and monitoring a degenerated tendon's response to treatment. The ANN CCR was higher than the highest CCR of 82.6% obtained by the LDA used in our previous work.