A fast algorithm for numerical identification of nonlinear heat transfer laws based on a natural linearization of the corresponding inverse problem is introduced. Its theoretical background is discussed, and the numerical tests show that it is suitable for problems with perturbed data. The proposed approach can be also used for other parameter identification problems, where one wants to recover an unknown nonlinear parameter β(u) from distributed noisy observations of the state u.