In this chapter, the convolutional mixing blind source separation problem in multipath channel environment is investigated. Firstly, the mathematical model of the convolutional mixing blind source separation problem is analyzed, and it is pointed out that there are two basic ideas for solving this problem, namely, the time-domain method and the frequency-domain method. On this basis, the convolutional blind separation algorithm in the time domain and the convolutional blind separation algorithm in the frequency domain are introduced respectively. For the time-domain methods, JBDun algorithm, F-JBDun algorithm and JBDus algorithm are introduced respectively, the principles of the algorithms are analyzed in detail and the algorithm steps are summarized. For the frequency-domain convolutional blind separation algorithm, the basic idea of the algorithm is introduced firstly. After analysis, it is found that the most critical problem of the frequency-domain blind separation algorithm is the resolution of the permutation ambiguity problem, so this chapter focuses on several sorting algorithms, including the amplitude correlation sorting algorithm, energy correlation sorting algorithm, and independent vector analysis algorithm, etc. Finally, the separation performance of different algorithms is simulated and analyzed.