In this paper, an effective memory-processor integrated architecture, called memory-based processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily integrated into any host system via memory interface. Specifically, the MPA system provides an efficient mechanism for its local memory accesses allowed by row and column bases, using hybrid row and column decoding, which is suitable for computation models of ANNs such as the accessing and alignment patterns given for matrix-by-vector operations. Mapping algorithms to implement the multilayer perceptron with backpropagation learning on the MPAA system are also provided. The proposed algorithms support both neuron and layer level parallelisms which allow the MPAA system to operate the learning phase as well as the recall phase in the pipelined fashion. Performance evaluation is provided by detailed comparison in terms of two metrics such as the cost and number of computation steps. The results show that the performance of the proposed architecture and algorithms is superior to those of the previous approaches, such as one-dimensional single-instruction multiple data (SIMD) arrays, two-dimensional SIMD arrays, systolic ring structures, and hypercube machines.