Abstract: We present a fast alternative for the Isomap algorithm. A set of quantizers is fit to the data and a neighborhood structure based on the competitive Hebbian rule is imposed on it. This structure is used to obtain low-dimensional description of the data by means of computing geodesic distances and multi dimensional scaling. The quantization allows for faster processing of the data. The speed-up as compared to Isomap is roughly quadratic in the ratio between the number of quan- tizers and the number of data points. The quantizers and neighborhood structure are use to map the data to the low dimensional space.