We propose a polygonal line based principal curve algorithm for nonlinear feature extraction, in which the nonlinearities among the multivariable data can be described by a set of local linear models. The proposed algorithm integrates the linear PCA approach with the polygonal line algorithm to represent complicated nonlinear data structure. Statistical redundancy elimination for high dimensional data is also discussed for describing the underlying principal curves without much loss of information among the original data sets. The polygonal line algorithm can produce robust and accurate nonlinear curve estimation for different multivariate data types, and it is helpful in reducing the computation complexity for existing principal curve approaches when the sample size is large.