The de-facto standard decoding algorithm for polar codes, successive cancellation list (SCL) decoding, is a breadth-first search algorithm. By keeping a list of candidate codewords, SCL decoding improves the performance as the list size L increases. However, generating this list requires storing L copies of internal log-likelihood ratios (LLRs). On the contrary, near-maximum likelihood (near-ML) decoding does not require internal LLRs. This algorithm returns the ML solution of the coded bits in a finite search space and shows superior error-rate performance for high code rates. In this paper, we propose a novel near-ML decoding algorithm based on the breadth-first search, a list ordered statistics decoding (List-OSD) algorithm, which estimates the coded bits starting from the most reliable ones. Simulation results show that the List-OSD exhibits 0.25 dB gain compared with SCL decoding at a frame error rate of 10(-3) for (128, 105) polar code with much less memory consumption for LLRs. Moreover, we design the pipelined hardware architecture of the proposed algorithm based on SMIC 65 nm technology, delivering a 353.14 Mbps throughput and 0.7 mm(2) area when L = 32. To the best of our knowledge, this is the first work that presents ASIC implementation results for OSD-based decoders.