Recently, various types of neural network models have been used successfully to applications in pattern recognition, control, signal processing, and so on. However, the previous models are not suitable for hardware implementation due to their complexity. In this paper, we present a survey of stochastic analysis for Langevine Comepetitive Learning Algorithm, known that it is easy for hardware im-plementation [1]. Since the Langevine competitive learning algorithm uses a time-invariant learning rate and a stochastic reinforcement term, it is necessary to analyze with stochastic differential or difference equation. The result of analysis verifies that the Langevine Comepetitive Learning process is equal to the standard Ornstein-Uhlenback process and has weak convergence property. The experimental results for Gaussian distributed data shows that the analysis provided in this paper is available.