Many incidents have occurred because industries have ignored past incidents or failed to learn lessons from the past. Incident databases provide an effective option for managing large amounts of information about the past incidents. Analysis of data stored in existing databases can lead to useful conclusions and reduction of chemical incidents and consequences of incidents. An incident database is a knowledge based system that can give an insight to the situation which led to an incident. Effective analysis of data from a database can help in development of information that can help reduce future incidents: cause of an incident, critical equipment, the type of chemical released, and the type of injury and victim. In this research, Hazardous Substances Emergency Events Surveillance (HSEES) database has been analyzed focusing on manufacturing events in Texas from 1993-2004. Between thirteen to sixteen states have participated in the HSEES incident reporting system and it does not include all the near miss incidents. Petroleum related incidents are also excluded from the HSEES system. Studies show that HSEES covers only 37% of all incidents in the US. This scaling ratio was used to estimate the total universe size.

Incident databases provide an excellent opportunity to study the repeated situations of incidents in the process industry. The databases give an insight into the situation which led to an incident, and if studied properly can help monitor the process, equipment and chemical involved more closely, and reduce the number of incidents in the future. This study examined a subset of incidents from National Response Center’s Incident database, focusing mainly on fixed facility incidents in Harris County, Texas from 1990 to 2002. Data mining has been used in the financial and marketing arena for many decades to analyze and find patterns in large amounts of data. Realizing the limited capabilities of traditional methods of statistics, more robust techniques of data mining were applied to the subset of data and interesting patterns of chemical involved, equipment failed, component involved, etc. were found. Further, patterns obtained by data mining on the subset of data were used in modifying probabilities of failure of equipment and developing a decision support system.