Moment has been one of the most popular techniques for image processing, pattern classification and computer vision. In this paper, we propose two VLSI architectures for computing the regular (geometric) moments and central moments. First, a one-dimensional systolic array is presented. In this architecture, a dynamic time delay controller is used for obtaining the correct data flow. It takes max(p, q);n#n#2 time units to compute the moments of order (p#q). If there are k images, the computational time will be k [max(p, q);n#n#2]. Second, a two-dimensional architecture is presented. It takes n#n#n!1#2#1"3n#2 time units to compute the moments of order (p#q). If there are k images, the computational time will be (k!1);2n#3n#2"2nk#n#2. The proposed approaches are much faster than the existing ones. If a uniprocessor is used, the time complexity is (p#q) n, and if there are k images, the computational time will be k(p#q) n. Finally, a VLSI architecture is presented for calculating the central moments. In this architecture, 3;n processing elements are used for calculating m , m , and m . The results are sent to a two-dimensional structure for computing central moments. It takes 2n#3#max(p, q)#2#n#n!1#1"4n#max(p, q)#5 time units to finish the calculation of the central moments. The important issue of VLSI design, algorithm partition, is also addressed. The basic idea of this paper can be extended to compute other kinds of moments easily. We have applied the moments for extracting the features of breast cancer biopsy images and classified them using neural networks. The 100% classification rate has been achieved.