Most products and services today are the results of several process stages and steps. With the current emphasis in industry on improved quality, control charts are widely used in process monitoring.  However,  conventional  statistical  process  control  (SPC)  techniques  focus  mostly  on  individual  stages  in  a  process  and  do  not  consider  disseminating  information  throughout the multiple stages of the process. Such techniques are ineffective in analyzing multistage  processes.  The  cause-selecting  chart  (CSC),  based  on  output  adjusted  for  the  effect of the incoming quality is an effective SPC tool for analyzing multistage processes. This paper discusses several important problems associated with conventional CSCs. First, the model relating input and output measures often needs to be estimated in practice before the CSC is implemented.  Little is known about the performance of the CSC when the model parameters are estimated. Second, the simple linear regression model widely discussed in the CSC is insufficient to capture the stochastic behavior of the output. In practice, the process disturbance  in  a  multistage  process  can  be  autocorrelated.  To  deal  with  autocorrelateddisturbance,  the  cumulative  score  (Cuscore)  chart  and  the  triggered  Cuscore  chart  are  proposed. Finally, extensions of the CSC to multivariate CSCs are discussed.