In this paper we report on the performance of a coupled oscillator based implementation of the HMAX image-processing pipeline. Within this pipeline we have used coupled oscillator arrays to replace traditional Boolean logic with a Degree-of-Match (DoM) function that measures the L2 distance squared between two vectors in an n-dimensional space. We show that this operation can be used in three stages of the pipeline: 1) as a substitute for convolution in filtering operations, 2) as a computational kernel for pattern matching, and 3) as a distance function in a nearest neighbor classification algorithm. In this study, we have modeled the performance of the latter two and report our recognition results over a test set from the Neo Vision2 image database.