We consider the problem of identifying volatility functions in Black-Scholes type equations from market data. Starting from an overview on different approaches, we focus on an optimal control approach in a Lagrangian framework. A regularized cost functional is minimized over a suitable set of admissible volatilities. We propose an algorithm that is based on sequential quadratic programming and present analytical as well as numerical results.