In some applications, such as data transmission, signal variance is unknown but constant. In such cases, adaptive quantizers using local variance estimation algorithms are not appropriate for the signal quantizations. The most suitable algorithms for this situation are those which learn the input signal variance. This work examines four variance learning algorithms for application in adaptive quantization. One of them, proposed by A. Gersho and D. J. Goodman, is a stochastic approximation algorithm which converges with probability one, when applied to adaptive quantization. The remaining two algorithms are modified versions of the first two, in order to obtain greater convergence speed. Finally, performance of these four adaptive quantizers, when used in DPCM systems, is analyzed through computer simulations.