In most of the applications of quality control, the quality of a process or a product is described by a relationship between the response variable and one or several independent variables. Such relationship is called a profile. Profile monitoring consists of two phases. The goal of phase I is to model process's performance and estimate model's parameters in a stable situation. The goal of the phase II is to promptly identify changes in the profile and the causes of such out-of-control states. In most real world issues, vagueness, imprecision and uncertainty in information are unavoidable. Hence, in this research we investigate and propose methods for process monitoring in phases I and II of processes with linear profiles in controlled situations with vague and fuzzy observed response variables. The proposed method for phase I of monitoring simple linear profiles with fuzzy observations is based on fuzzy change point technique. The efficiency of the method is evaluated by means of simulation and based on the probability of out-of-control signal criterion. Results show that not only is this method efficient, but it has also the advantage over competing methods that it provides the analyst with an estimate of the point of divergence of the process from control. This feature enhances identification and deletion of out-of-control points to get a set of in-control data. Two methods are proposed for the phase II of profile monitoring with fuzzy observations. In these methods, initially the profile parameters are determined by means of fuzzy regression technique. Afterwards, fuzzy EWMA and fuzzy Hotelling 's T 2 statistics and fuzzy hypothesis testing are employed. To this end, fuzzy EWMA and fuzzy Hotelling 's T 2 statistics are developed using extension principle. The efficiency of methods are tested by means of simulation and based on the average run length. The results show that the proposed method based on fuzzy EWMA statistic and fuzzy Hotelling 's T 2 statistic are respectively efficient for identification of small changes and for identification of medium and significant changes. Considering the sensitivity of the process and the goals of the analysis, it is possible to use either one of the methods or a combination of the two to monitor profiles of processes that their quality characteristics are vague, imprecise, and uncertain.