In speaker verification, the mismatches between the training speech and the testing speech can greatly affect the robustness of classification algorithms, and the mismatches are mainly caused by the changes in the noise types and the signal to noise ratios. This thesis aims at finding the most robust classification methods under multi-noise and multiple signal to noise ratio conditions. Comparison of several well-known state of the art classification algorithms and features in speaker verification are made through examining the performance of small-set speaker verification system (e.g. voice lock for a family). The effect of the testing speech length is also examined. The i-vector/Probabilistic Linear Discriminant Analysis method with compensation strategies is shown to provide a stable performance for both previously seen and previously unseen noise scenarios, and a C++ implementation with online processing and multi-threading is developed for this approach.