The prime concern for a business organization is to supply quality services to the customers without any delay or interruption so to establish a good reputation among the customer’s and competitors. On-time delivery of a customers order not only builds trust in the business organization but is also cost effective. Therefore, there is a need is to monitor complex business processes though automated systems which should be capable during execution to predict delay in processes so as to provide a better customer experience. This online problem has led us to develop an automated solution using machine learning algorithms so as to predict possible delay in business processes. The core characteristic of the proposed system is the extraction of generic process event log, graphical and sequence features, using the log generated by the process as it executes up to a given point in time where a prediction need to be made (referred to here as cut-off time); in an executing process this would generally be current time. These generic features are then used with Support Vector Machines, Logistic Regression, Naive Bayes and Decision trees to predict the data into on-time or delayed processes. The experimental results are presented based on real business processes evaluated using various metric performance measures such as accuracy, precision, sensitivity, specificity, F-measure and AUC for prediction as to whether the order will complete on-time when it has already been executing for a given period.