This paper describes a vision-based system that monitors the yarn density of woven fabrics on-line. The system is described in terms of its two principal modules, namely, the image acquisition and the image analysis subsystems. The image acquisition subsystem is implemented with standard components on a low-cost personal computer platform. These components consist of a line-scan camera, a DSP-based image acquisition and processing card, and a host personal computer. The image acquisition process is controlled by a software module that runs on the DSP board and accumulates a 2D image suitable for the density measurement algorithm. The image analysis subsystem, which also runs on the DSP board, implements a novel, yet straightforward, algorithm that utilizes the discrete Fourier transform for monitoring the yarn density of the fabrics from the acquired images. In this algorithm, the Fourier spectrum of the images is covered by contiguous, concentric annular regions that have a prespecified width. THe spectrum values within each annular region are summed, normalized, and subsequently used to produce a 1D signature. Simple statistics of the obtained signatures are the basis for characterizing the fabric in terms of its yarn density. The described system is tested on seven fabrics with common properties but varying yarn densities and has shown to be accurate within 2 yarns per inch in either direction. It is also shown that the obtained accuracy, which is primarily a function of the image resolution, can be greatly improved.