Missing data problems are common and difficult to handle in data analysis. Ad hoc methods such as simply removing cases with missing values can lead to invalid analysis results. In this paper, we consider a functional linear regression model with partially observed covariate and missing values in the response. We use a reconstruction operator that aims at recovering the missing parts of the explanatory curves, then we are interested in regression imputation method of missing data on the response variable, using functional principal component regression to estimate the functional coe cient of the model. We study the asymptotic behavior of the prediction error when missing values in a original dataset are imputed by multiple sets of plausible values.