Hip fracture due to osteoporosis (OP) and hip osteoarthritis (OA) are both important causes of locomotor morbidity in the elderly population. In osteoporosis, bone mass gradually decreases until the skeleton is too fragile to support the body and a fracture occurs, typically in the femur, wrist or spine. In osteoarthritis, there is a proliferation of bone, leading to a stiffening of the tissue. Current clinical methods for assessment of bone changes in these disorders largely depend on assessing bone mineral density. However, this does not provide any information about bone structure, which is considered to be an equally important factor in assessing bone quality. This paper presents a novel approach for computer analysis of trabecular (or cancellous) bone structure. The technique uses a Fourier transform to generate a "spectral fingerprint" of an image. Principal components analysis is then applied to identify key features from the Fourier transform and this information is passed to a neural network for classification. Testing this on a series of 100 histological sections of trabecular bone from patients with OP and OA and a normal group correctly classified over 90% of the OP group with an overall accuracy of 77%-84%. Such high success rates on a small group suggest that this may provide a simple, but powerful, method for identifying alterations in bone structure.