Analog Computing Arrays (ACAs) provide a computation system capable of performing a large number of multiply and add operations in an analog form. This system can therefore implement several computation algorithms that are currently realized using Digital Signal Processors (DSPs) who have an analogues accumulate and add functionality. DSPs are generally preferred for signal processing because they provide an environment that permits programmability once fabricated. ACA systems propose to offer similar functionality by providing a programmable and reconfigurable analog system. ACAs inherent parallelism and analog efficiency present several advantages over DSP implementations of the same systems. The computation power of an ACA system is directly proportional to the number of computing elements used in the system. Array size is limited by the number of computation elements that can be managed in an array. This number is continually growing and as a result, is permitting the realization of signal processing systems such as real-time speech recognition, image processing, and many other matrix like computation systems. This research provides a systematic process to implement, program, and use the computation elements in large-scale Analog Computing Arrays. This infrastructure facilitates the incorporation of ACA without the current headaches of programming large arrays of analog floating-gates from off-chip, currently using multiple power supplies, expensive FPGA controllers/computers, and custom Printed Circuit Board (PCB) systems. Proof of the flexibility and usefulness of ACAs has been demonstrated by the construction of two systems, an Analog Fourier Transform and a Vector Quantizer.