We　develope　an　M-estimator　for　partially　linear　models　in　which　the　nonparametric　component　is　subject　to　various　shape　constraints.　Bernstein　polynomials　are　used　to　approximate　the　unknown　nonparametric　function,　and　shape　constraints　are　imposed　on　the　coefficients.　Asymptotic　normality　of　regression　parameters　and　the　optimal　rate　of　convergence　of　the　shape-restricted　nonparametric　function　estimator　are　established　under　very　mild　conditions.　Some　simulation　studies　and　a　real　data　analysis　are　conducted　to　evaluate　the　finite　sample　performance　of　the　proposed　method.