We consider least squares estimators of the finite regression parameter $α$ in the single index regression model $Y=ψ(α^T X)+ε$, where $X$ is a $d$-dimensional random vector, $\E(Y|X)=ψ(α^T X)$, and where $ψ$ is monotone. It has been suggested to estimate $α$ by a profile least squares estimator, minimizing $\sum_{i=1}^n(Y_i-ψ(α^T X_i))^2$ over monotone $ψ$ and $α$ on the boundary $S_{d-1}$of the unit ball. Although this suggestion has been around for a long time, it is still unknown whether the estimate is $\sqrt{n}$ convergent. We show that a profile least squares estimator, using the same pointwise least squares estimator for fixed $α$, but using a different global sum of squares, is $\sqrt{n}$-convergent and asymptotically normal. The difference between the corresponding loss functions is studied and also a comparison with other methods is given.