The objective of clustering, or unsupervised classification, is to partition a set of observations into different groups, or clusters, based on their similarities. Following Hartigan, a cluster is defined as a connected component of an upper level set of the underlying density. In this talk, we introduce a spectral clustering algorithm on estimated level sets, and we establish its strong consistency. We also discuss the estimation of the number of connected components of density level sets.