###### Abstract  
Control charts (CCs) are one of the most important techniques in statistical process control (SPC) used to monitor the behavior of critical variables. SPC is based on the averages of the samples taken. In this way, not every measurement is observed, and errors in measurements or out-of-control behaviors that are not shown graphically can be hidden. This research proposes a novel segmented control chart (SCC) that considers each measurement of the samples, expressed in matrix form. The vision system technique is used to segment measurements by shading and segmenting into binary values based on the control limits of SPC. Once the matrix is segmented, the seven main features of the matrix are extracted using the translation-, scale-, and rotation-invariant Hu moments of the segmented matrices. Finally, a grouping is made to classify the samples in clear and simple language as excellent, good, or regular using the k-means algorithm. The results visually display the total pattern behavior of the samples and their interpretation when they are classified intelligently. The proposal can be replicated in any production sector and strengthen the control of the sampling process. SPC; control chart; Hu moments; feature extraction; vision systems; k-means +
Footnote †: 2020: _Applied sciences_
fall within the established three-sigma limits. The interpretation of an unstable process is defined by points that fall outside the regulatory limits or by points exhibiting non-random trends or clusters. Process analysis is necessary when such points exist in the control chart, allowing for the identification and elimination of identifiable causes of signal instability based on expert knowledge. In industrial practice, it often occurs that even in a stable state of process technology, the output variables exhibit inherent variability that cannot be eliminated. A CC is a graphical tool that displays the behavior of a given selection feature and requires the input of experts during the process to analyze the behavior of the data and subjectively classify whether the process is under control or not, as experts interpret whether any special causes are present based on their interpretation. The approach proposed in this research presents a novel segmented control chart (SCC) that intelligently and automatically interprets and classifies data behavior based on all sample measurements rather than just plotting the average, as is currently done with CC. Over time, research has been carried out to strengthen CCs. For example, [PERSON] [4] mentioned that it is possible to construct a control chart of any statistic with an unknown continuous distribution and variable control limits based on the non-parametric bootstrap percentile method. [PERSON] et al. [5] integrated two critical techniques of statistical process control (SPC) and engineering process control (EPC) to improve quality through online monitoring, regulation, and correction actions. [PERSON] et al. [6] applied SPC directly on the bus chassis production line workstation. The SPC is a preventive quality tool that has the main advantage of detecting problems before they arise. [PERSON] et al. [7] made modifications to the SPC. In the first part, they integrate multivariate approaches and data extraction tools that include machine learning methods. The second part integrates non-parametric methods of SPC and carries out their practical application. [PERSON] [8] presented an integrated model of SPC. The average total cost is used, and its first derivative is obtained. A genetic algorithm subject to quality penalties is then developed. The Markov chain approach is also used to model the integrated process. [PERSON] et al. [9] integrated SPC and PM strategies to enhance the manufacturing environment. PM applies to those abnormal processes to extract actual process flow from event logs, and SPC identifies out-of-control processes that deviate from specification limits.