  We present a detailed description of the idea and procedure for the newly
proposed Monte Carlo algorithm of tuning the critical point automatically,
which is called the probability-changing cluster (PCC) algorithm [Y. Tomita and
Y. Okabe, Phys. Rev. Lett. {\bf 86} (2001) 572]. Using the PCC algorithm, we
investigate the three-dimensional Ising model and the bond percolation problem.
We employ a refined finite-size scaling analysis to make estimates of critical
point and exponents. With much less efforts, we obtain the results which are
consistent with the previous calculations. We argue several directions for the
application of the PCC algorithm.
